Mixture Model

Dimensionality reduction can be efficiently achieved by generative latent variable models such as probabilistic principal component analysis (PPCA) or independent component analysis (ICA), aiming to extract a reduced set of variables (latent variables) from the original ones. In most cases, the learning of these methods is achieved within the unsupervised framework where only unlabeled samples are used. In this paper we investigate the possibility of estimating independent factor analysis model (IFA) and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. In the basic IFA model, latent variables are only recovered from their linear observed mixtures (original features). Both the mapping matrix (assumed to be linear) and the latent variable densities (that are assumed to be mutually independent and generated according to mixtures of Gaussians) are learned from observed data. We propose to learn this model within semisupervised framework where the likelihood of both labeled and unlabeled samples is maximized by a generalized expectationmaximization (GEM) algorithm. Experimental results on real data sets are provided to demonstrate the ability of our approach to find law dimensional manifold with good explanatory power.

Recent advances in statistical software have led to the rapid diffusion of new methods for modelling longitudinal data. Multilevel (also known as hierarchical or random effects) models for binary outcomes have generally been based on a logistic-normal specification, by analogy with earlier work for normally distributed data. The appropriate application and interpretation of these models remains somewhat unclear, especially when compared with the computationally more straightforward semiparametric or 'marginal' modelling (GEE) approaches. In this paper we pose two interrelated questions. First, what limits should be placed on the interpretation of the coefficients and inferences derived from random-effect models involving binary outcomes? Second, what diagnostic checks are appropriate for evaluating whether such random-effect models provide adequate fits to the data? We address these questions by means of an extended case study using data on adolescent smoking from a large cohort study. Bayesian estimation methods are used to fit a discrete-mixture alternative to the standard logistic-normal model, and posterior predictive checking is used to assess model fit. Surprising parallels in the parameter estimates from the logistic-normal and mixture models are described and used to question the interpretability of the socalled 'subject-specific' regression coefficients from the standard multilevel approach. Posterior predictive checks suggest a serious lack of fit of both multilevel models. The results do not provide final answers to the two questions posed, but we expect that lessons learned from the case study will provide general guidance for further investigation of these important issues.

This study examined the developmental trajectories of parent-child relationships in adolescence, especially with respect to changes in support levels and negativity, and analyzed if and how these trajectories were associated with the subsequent quality of romantic relationships in young adulthood. A sample of 145 German subjects was followed across six waves (i.e., ages 14, 15, 16, 17, 21, and 23 years). Growth mixture modeling revealed three developmental trajectories of parent-child relationships across adolescence (i.e., normative, increasingly negative, and decreasingly negative/distant), which were associated with the quality of romantic outcomes at two times in emerging adulthood. Earlier mother-adolescent relationships were distinctively linked with connectedness and sexual attraction experienced in young adults' romantic relationships. Distant father-child relationships during adolescence were linked with the child's later anxious love.

In this paper we present a framework for realtime online signature verification scenarios. The proposed framework is based on state-of-the-art feature extraction and Gaussian Mixture Model (GMM) classification. While our signature verification library is generally applicable to any input device using digital pens, we have implemented verification scenarios using the Anoto digital pen. As such our automated signature verification framework becomes an interesting commodity for industry, because the Anoto SDK is easy to apply and the GMM-based classification can be seamlessly integrated. The novelty of this work is the application of our framework that takes real-time online signature verification to every scenario where digital pens may potentially be used. In this paper we describe several scenarios where our framework has been applied, including signatures in financial contracts or ordering processes. We also propose a general approach to integrate the GMM-descriptions into electronic ID-cards in order to also store behavioral biometrics on these cards. In experiments we have measured the performance of the signature verification system when skilled forgeries were present. The interest shown by our partner financial institutions and the results of our initial evaluations indicate that our signature verification framework suits exactly the demands of our clients.

Objective Fetal nuchal translucency (NT) thickness increases with crown-rump length (CRL). In screening for chromosomal defects patient-specific risks are derived by multiplying the a priori maternal age-related risk by a likelihood ratio, determined from the deviation of the measured NT from the expected median. To quantify this deviation the measured NT is either subtracted (delta NT) or divided by the expected median (multiple of the median method, MoM). This study examines the validity of these methods. Methods NT was prospectively measured at 11 + 0 to 13 + 6 weeks in screening for chromosomal defects. The distribution of NT in euploid and chromosomally abnormal fetuses was examined.

This paper presents a discussion on semi-supervised learning of probabilistic mixture model classifiers for face detection. We present a theoretical analysis of semi-supervised learning and show that there is an overlooked fundamental difference between the purely supervised and the semisupervised learning paradigms. While in the supervised case, increasing the amount of labeled training data is always seen as a way to improve the classifier's performance, the converse might also be true as the number of unlabeled data is increased in the semi-supervised case. We also study the impact of this theoretical finding on Bayesian network classifiers, with the goal of avoiding the performance degradation with unlabeled data. We apply the semisupervised approach to face detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers for face detection with a small labeled set and a large unlabeled set.

Objective: To compare three statistical strategies for classifying positive treatment response based on a dimensional measure (Yale Global Tic Severity Scale [YGTSS]) and a categorical measure (Clinical Global Impression-Improvement [CGI-I] scale). Method: Subjects (N = 232; 69.4% male; ages 9-69 years) with Tourette syndrome or chronic tic disorder participated in one of two 10-week, randomized controlled trials comparing behavioral treatment to supportive therapy. The YGTSS and CGI-I were rated by clinicians blind to treatment assignment. We examined the percent reduction in the YGTSS-Total Tic Score (TTS) against Much Improved or Very Much Improved on the CGI-I, computed a signal detection analysis (SDA) and built a mixture model to classify dimensional response based on the change in the YGTSS-TTS. Results: A 25% decrease on the YGTSS-TTS predicted positive response on the CGI-I during the trial. The SDA showed that a 25% reduction in the YGTSS-TTS provided optimal sensitivity (87%) and specificity (84%) for predicting positive response. Using a mixture model without consideration of the CGI-I, the dimensional response was defined by 23% (or greater) reduction on the YGTSS-TTS. The odds ratio (OR) of positive response (OR = 5.68, 95% CI = [2.99, 10.78]) on the CGI-I for behavioral intervention was greater than the dimensional response (OR = 2.86, 95% CI = [1.65, 4.99]). Conclusion: A 25% reduction on the YGTSS-TTS is highly predictive of positive response by all three analytic methods. For trained raters, however, tic severity alone does not drive the classification of positive response. Clinicaltrials.gov identifiers: NCT00218777; NCT00231985.

Mixture modeling within the context of pharmacokinetic (PK)/pharmacodynamic (PD) mixed effects modeling is a useful tool to explore a population for the presence of two or more subpopulations, not explained by evaluated covariates. At present, statistical tests for the existence of mixed populations have not been developed. Therefore, a simulation study was undertaken to evaluate mixture modeling with NONMEM and explore the following questions. First, what is the probability of concluding that a mixed population exists when there truly is not a mixture (false positive significance level)? Second, what is the probability of concluding that a mixed population (two subpopulations) exists when there is truly a mixed population (power), and how well can the mixture be estimated, both in terms of the population parameters and the individual subject classification. Seizure count data were simulated using a Poisson distribution such that each subject's count could decrease from its baseline value, as a function of dose via an Emax model. The dosing design for the simulation was based on a trial with the investigational anti-epileptic drug pregabalin. Four hundred and forty seven subjects received pregabalin as add on therapy for partial seizures, each with a baseline seizure count and up to three subsequent seizure counts. For the mixtures, the two subpopulations were simulated to differ in their Emax values and relative proportions. One subpopulation always had its Emax set to unity (Emax hi), allowing the count to approach zero with increasing dose. The other subpopulation was allowed to vary in its Emax value (Emax lo=0.75, 0.5, 0.25, and 0) and in its relative proportion (pr) of the population (pr=0.05, 0.10, 0.25, and 0.50) giving a total of 4 ⋅ 4=16 different mixtures explored. Three hundred data sets were simulated for each scenario and estimations performed using NONMEM. Metrics used information about the parameter estimates, their standard errors (SE), the difference between minimum objective function (MOF) values for mixture and non-mixture models (MOF(δ)), the proportion of subjects classified correctly, and the estimated conditional probabilities of a subject being simulated as having Emax lo (Emax hi) given that they were estimated as having Emaxlo (Emax hi) and being estimated as having Emaxlo (Emax hi) given that they were simulated as having Emax lo (Emax hi). The false positive significance level was approximately 0.04 (using all 300 runs) or 0.078 (using only those runs with a successful covariance step), when there was no mixture. When simulating mixed data and for those characterizations with successful estimation and covariance steps, the median (range) percentage of 95% confidence intervals containing the true values for the parameters defining the mixture were 94% (89–96%), 89.5% (58–96%), and 95% (92–97%) for pr, Emax lo, and Emax hi, respectively. The median value of the estimated parameters pr, Emax lo (excluding the case when Emax lo was simulated to equal 0) and Emax hi within a scenario were within ±28% of the true values. The median proportion of subjects classified correctly ranged from 0.59 to 0.96. In conclusion, when no mixture was present the false positive probability was less than 0.078 and when mixtures were present they were characterized with varying degrees of success, depending on the nature of the mixture. When the difference between subpopulations was greater (as Emax lo approached zero or pr approached 0.5) the mixtures became easier to characterize.

This longitudinal study examined Latino adolescents’ feelings of loneliness and the repercussions of loneliness for later educational success. Participants were 640 Latino students (56% girls, 62% Mexican/Mexican–American) who reported on loneliness across the first 2 years of high school. Growth mixture modeling identified three distinct loneliness trajectory classes for the Latino adolescents—consistently low, chronically high, and low but increasing. Language brokering, language use, and school mobility emerged as predictors of class membership. Increasingly and chronically lonely youth experienced academic difficulty, both in terms of academic progress and exit exam success, but support from friends served as a buffer of the negative relationship between loneliness and academic success. This study highlights the pernicious effects of loneliness and suggests promoting prosocial friendship support as a means of facilitating more positive academic outcomes for Latino youth.

Phylogenetic mixture models are statistical models of character evolution allowing for heterogeneity. Each of the classes in some unknown partition of the characters may evolve by different processes, or even along different trees. The fundamental question of whether parameters of such a model are identifiable is difficult to address, due to the complexity of the parameterization. We analyze mixture models on large trees, with many mixture components, showing that both numerical and tree parameters are indeed identifiable in these models when all trees are the same. We also explore the extent to which our algebraic techniques can be employed to extend the result to mixtures on different trees.

We examine three pattern-mixture models for making inference about parameters of the distribution of an outcome of interest Y that is to be measured at the end of a longitudinal study when this outcome is missing in some subjects. We show that these pattern-mixture models also have an interpretation as selection models. Because these models make unverifiable assumptions, we recommend that inference about the distribution of Y be repeated under a range of plausible assumptions. We argue that, of the three models considered, only one admits a parameterization that facilitates the examination of departures from the assumption of sequential ignorability. The three models are nonparametric in the sense that they do not impose restrictions on the class of observed data distributions. Owing to the curse of dimensionality, the assumptions that are encoded in these models are sufficient for identification but not for inference. We describe additional flexible and easily interpretable assumptions under which it is possible to construct estimators that are well behaved with moderate sample sizes. These assumptions define semiparametric models for the distribution of the observed data. We describe a class of estimators which, up to asymptotic equivalence, comprise all the consistent and asymptotically normal estimators of the parameters of interest under the postulated semiparametric models. We illustrate our methods with the analysis of data from a randomized clinical trial of contracepting women.

This paper presents a novel solution to the difficult task of both detecting and estimating the 3D pose of humans in monoscopic images. The approach consists of two parts. Firstly the location of a human is identified by a probabalistic assembly of detected body parts. Detectors for the face, torso and hands are learnt using adaBoost. A pose likliehood is then obtained using an a priori mixture model on body configuration and possible configurations assembled from available evidence using RANSAC. Once a human has been detected, the location is used to initialise a matching algorithm which matches the silhouette and edge map of a subject with a 3D model. This is done efficiently using chamfer matching, integral images and pose estimation from the initial detection stage. We demonstrate the application of the approach to large, cluttered natural images and at near framerate operation (16fps) on lower resolution video streams.

This paper presents a novel solution to the difficult task of both detecting and estimating the 3D pose of humans in monoscopic images. The approach consists of two parts. Firstly the location of a human is identified by a probabalistic assembly of detected body parts. Detectors for the face, torso and hands are learnt using adaBoost. A pose likliehood is then obtained using an a priori mixture model on body configuration and possible configurations assembled from available evidence using RANSAC. Once a human has been detected, the location is used to initialise a matching algorithm which matches the silhouette and edge map of a subject with a 3D model. This is done efficiently using chamfer matching, integral images and pose estimation from the initial detection stage. We demonstrate the application of the approach to large, cluttered natural images and at near framerate operation (16fps) on lower resolution video streams.

Although aggressive medical treatment protocols have led to 80% 5-year survival rates for most childhood cancers, many long-term survivors experience multiple troubling symptoms. Using data from 100 adult survivors of childhood cancers (ACC-survivors), we used latent variable mixture modeling to generate unique subgroups of survivors based on their experiences with a cluster of eight symptoms: lack of energy, worry, pain, difficulty sleeping, feeling irritable, feeling nervous, difficulty concentrating, and feeling sad (as measured by the Memorial Symptom Assessment Scale). We also examined factors that were likely to predict subgroup membership (chronic health conditions, health-promoting lifestyle, and demographic variables) and determined the extent to which satisfaction with quality of life (QoL) varied across the subgroups. The final mixture model included three subgroups of ACC-survivors: high symptoms (HS) (n=21), moderate symptoms (MS) (n=45), and low symptoms (LS) (n=34). ACC-survivors who reported at least one chronic health condition were six times as likely to be classified in the HS subgroup as compared to the LS subgroup. Mean health-promoting lifestyle scores were lowest in the HS subgroup and highest in the LS subgroup. Differences in QoL among the subgroups were statistically significant, thus validating that the subgroups were characterized uniquely for identifying those symptoms with highest life impact. To our knowledge, we are the first to identify distinct subgroups of ACC-survivors differentiated by symptom cluster experience profiles. The findings warrant additional research to confirm the subgroup-specific symptom cluster experience profiles in larger studies of ACC-survivors.

In many applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. Here we consider partially monotone problems, where the target variable depends monotonically on some of the predictor variables but not all. We propose an approach to build partially monotone models based on the convolution of monotone neural networks and kernel functions. The results from simulations and a real case study on house pricing show that our approach has significantly better performance than partially monotone linear models. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

The subspace Gaussian mixture model (SGMM) has been exploited for cross-lingual speech recognition. The general motivation is that the subspace parameters can be estimated on multiple source languages and then transferred to the target language. In this work, we investigate an extension to SGMM, referred to as subspace mixture model (SMM), in which subspace parameters on the target language are casted as a linear mixture of the subspaces derived from source languages. This approach reduces the number of SGMM model parameters, while retaining the flexibility of subspace learning on the target language. Experiments show that the proposed SMM method outperforms SGMM significantly when the target language has limited training data.

In this study links between spousal and parent-child relationships among Finnish (n = 157 couples) and Dutch (n = 276 couples) dual earners with young children were examined using paired questionnaire data. Variable-oriented analyses (structural equation modeling with a multigroup procedure) supported the spillover hypothesis, as higher levels of satisfaction in the spousal relationship were related to higher quality in

Deadlegs are defined as the inactive portion of the pipe where the flow is stagnant. Corrosion in deadlegs occurs as a result of water separation due to the very low flow velocity. The present work provides an investigation of the effect of deadleg geometry and average flow velocity on flow field and oil/water separation in deadlegs. The investigation is based on the solution of the mass and momentum conservation equations of an oil/water mixture together with the volume fraction equation for the secondary phase. A fluid flow model based on the time-averaged governing equations of 3-D turbulent flow has been developed. An algebraic slip mixture model is utilized for the calculation of the two immiscible fluids (water and crude oil). The model solves the continuity and momentum equations for the mixture, and the volume fraction equation for the secondary phase utilizing an algebraic expression for the relative velocity. Flow visualization experiments were conducted in order to validate the numerical procedure. Good agreement was obtained between the calculated and measured flow patterns. Results are obtained for different lengths of the deadleg. The considered fluid mixture contains 90% oil and 10% water (by volume). The inlet flow velocity ranges from 0.2 to 10 m/s and the deadleg length to diameter ratio (L/D) ranges from 1 to 10. The results showed that the size of the stagnant fluid region increases with the increase of L/D and decreases with the increase of inlet velocity. The results also indicated that the water volumetric concentration increases with the increase of L/D and influenced by the deadleg geometry.

Litter is frequently present within vegetation canopies and thus contributes to the overall spectral response of a canopy. Consequently, litter will affect spectral indices designed to be sensitive to green vegetation, soil brightness or other features. The main objectives of the current research were to 1) evaluate the spectral properties of green vegetation and litter and 2) quantify the effect of standing litter on the performance of spectral indices. The SAIL (scattering by arbitrarily inclined leaves) model was used to generate canopy reflectance "mixtures" and to estimate fractions of absorbed photosynthetically active radiation (fAPAR) with varying leaf area index (LAI), soil background, combinations of vegetation component spectral properties, and one or two horizontal vegetation layers. Spectral measurements of different bare soils and mature green and senescent leaves of representative plant species at the HAPEX-Sahel (Hydrological Atmospheric Pilot Experiment) study sites were used as input. The normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SA VI), and the modified NDVI (MNDVI) and mixture model spectral indices were selected to evaluate their performance with respect to standing litter and green vegetation mixtures. Spectral reflectance signatures of leaf litter varied significantly, but strongly resembled soil spectral characteristics. The biophysical parameters (LAI, fAPAR), derived from spectral vegetation indices, tended to be overestimated for randomly distributed, sparse green and litter vegetation cover mixtures, and underestimated for randomly distributed dense green and litter vegetation cover mixtures. All spectral indices and their biophysical interpretation were significantly altered by variability in 1) green leaf, leaf litter, and bark optical properties, 2) the amount and position of standing leaf litter, 3) leaf angle distribution, and 4) soil background. The NDVI response to these variables was inconsistent, and was the most affected by litter. The spectral mixture model indices, designed to be sensitive to litter, were shown to be promising for the identification of litter present among different ecosystems.

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p ¼ 0.17) or between above-ground biomass and NDVI (p ¼ 0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE ¼ 43 g m -2 for NDVI and RMSE ¼ 43 g m -2 for EVI, both p , 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.

It is shown that the classical taxonomy of missing data models, namely missing completely at random, missing at random and informative missingness, which has been developed almost exclusively within a selection modelling framework, can also be applied to pattern-mixture models. In particular, intuitively appealing identifying restrictions are proposed for a pattern-mixture MAR mechanism.

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Number sense development was tracked from the beginning of kindergarten through the middle of first grade, over six time points. Children (n = 277) were then assessed on general math achievement at the end of first grade. Number sense performance in kindergarten, as well as number sense growth, accounted for 66 percent of the variance in first-grade math achievement. Background characteristics of income status, gender, age, and reading ability did not add explanatory variance over and above growth in number sense. Even at the beginning of kindergarten, number sense was highly correlated with end of first-grade math achievement (r = 0.70). Clarifying the observed slope effect, general growth mixture modeling showed that children who started kindergarten with low number sense but made moderate gains by the middle of kindergarten had higher first-grade math achievement than children who started out with similarly low number sense with flat growth. The majority of children in the low/flat growth class were from low-income families. The findings indicate that screening early number sense development is useful for identifying children who will face later math difficulties or disabilities.

This article introduces a new SAS procedure written by the authors that analyzes longitudinal data (developmental trajectories) by fitting a mixture model. The TRAJ procedure fits semiparametric (discrete) mixtures of censored normal, Poisson, zero-inflated Poisson, and Bernoulli distributions to longitudinal data. Applications to psychometric scale data, offense counts, and a dichotomous prevalence measure in violence research are illustrated. In addition, the use of the Bayesian information criterion to address the problem of model selection, including the estimation of the number of components in the mixture, is demonstrated. demonstrates a new SAS procedure, called TRAJ, developed by the authors for estimating developmental trajectories. The procedure is based on a semiparametric, group-based modeling strategy. Technically, the model is a mixture of probability distributions that are suitably specified to describe the data to be analyzed. The approach is intended to complement two well-established methods for analyzing developmental trajectories-hierarchical modeling Raudenbush 1987, 1992; and latent growth curve modeling . In hierarchical modeling, individual variation in developmental trajectories, which are commonly called growth curves, are captured by a random coefficients modeling strategy.

A common method for real-time segmentation of moving regions in image sequences involves "background subtraction," or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model.

Traditionally multi-variate normal distributions have been the staple of data modeling in most domains. For some domains, the model they provide is either inadequate or incorrect because of the disregard for the directional components of the data. We present a ...

A novel semiparametric regression model for censored data is proposed as an alternative to the widely used proportional hazards survival model. The proposed regression model for censored data turns out to be flexible and practically meaningful. Features include physical interpretation of the regression coefficients through the mean response time instead of the hazard functions, and a rigorous proof of consistency of the posterior distribution. It is shown that the regression model obtained by a mixture of parametric families, has a proportional mean structure (as in an accelerated failure time models). The statistical inference is based on a nonparametric Bayesian approach that uses a Dirichlet process prior for the mixing distribution. Consistency of the posterior distribution of the regression parameters in the Euclidean metric is established. Finite sample parameter estimates along with associated measure of uncertainties can be computed by a MCMC method. Simulation studies are p...

Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

A general binomial mixture model is proposed for the species accumulation function based on presence-absence (incidence) of species in a sample of quadrats or other sampling units. The model covers interpolation between zero and the observed number of samples, as well as extrapolation beyond the observed sample set. For interpolation (samplebased rarefaction), easily calculated, closed-form expressions for both expected richness and its confidence limits are developed (using the method of moments) that completely eliminate the need for resampling methods and permit direct statistical comparison of richness between sample sets. An incidence-based form of the Coleman (random-placement) model is developed and compared with the moment-based interpolation method. For extrapolation beyond the empirical sample set (and simultaneously, as an alternative method of interpolation), a likelihood-based estimator with a bootstrap confidence interval is described that relies on a sequential, AIC-guided algorithm to fit the mixture model parameters. Both the moment-based and likelihood-based estimators are illustrated with data sets for temperate birds and tropical seeds, ants, and trees. The moment-based estimator is confidently recommended for interpolation (sample-based rarefaction). For extrapolation, the likelihood-based estimator performs well for doubling or tripling the number of empirical samples, but it is not reliable for estimating the richness asymptote. The sensitivity of individual-based and sample-based rarefaction to spatial (or temporal) patchiness is discussed.

Mixture models: Inference and applications to clustering. New York, N.Y: M. Dekker. Editorial: recent developments in mixture models 10 Sep 2013 . McLachlan, Geoffrey J. and Basford, Kaye E. Mixture models : inference and applications to clustering. New York, United States: Marcel Dekker, Catalogue -Mixture models : inference and applications to clustering Mixture Models: Inference and Applications to Clustering (Statistics . Model-based clustering consists of fitting a mixture model to data and identifying . (1988), Mixture Models: Inference and Applications to Clustering, New York:. Medical Applications of Finite Mixture Models -Google Books Result Title: Mixture models. Inference and applications to clustering. Authors: McLachlan, Geoffrey J.; Basford, Kaye E. Publication: Statistics: Textbooks and a variety of sources of difficulties in statistical inference using mixture models and uses a . An application of the proposed model to real data is also given. models (Yashin et al., 1995) are appropriate for clustered survival data, where one Mixture Models: Inference and Applications to Clustering. Dekker Finite Mixture Models, Willey Series in Probability and Statistics. G McLachlan, D Peel. John Wiley Mixture models. Inference and applications to clustering. Mixture models : inference and applications to clustering -WorldCat Mixture Models: Inference and Applications to Clustering Statistics: a Series of Textbooks and Monogrphs: Amazon.de: G. J. McLachlan, K. E. Basford, Geoffrey ?Download PDF -Springer The normal mixture model-based approach to this problem as developed in Aitkin . K. E. (1988), Mixture Models: Inference and Applications to Clustering, New. Mixture models. Inference and applications to clustering Amazon.com: Mixture Models (Statistics: A Series of Textbooks and Monographs) a Kindle? Get your Kindle here, or download a FREE Kindle Reading App. Advances in Mixture Models -Department of Statistics -Stanford . Hunt (1996) implemented the finite mixture model approach to clustering in a program . In their monograph on mixture models and their application to clustering, .. but would appear to resist any form of statistical inference for the value of K. Mixture modelling for cluster analysis Mixture Models: Inference and Appli-cations to Clustering. New York: cluster analysis: there is no a priori knowledge of a group structure and one wishes to Applications of finite mixture models to real data sets are given in Chapter 3. The. Combining Mixture Components for Clustering -Statistics ?AbeBooks.com: Mixture Models: Inference and Applications to Clustering: Hardcover in good condition. Illustrated green boards with white lettering on front and Booktopia -Mixture Models, Inference and Applications to Clustering . We then need a model that is sufficiently rough, which will not be too influenced by relatively small variations in the data. In practice, the mixture model is used REVIEWS Geoffroy J. McLachlan and Kaye E. Basford. Mixture Cluster analysis via a finite mixture model approach is considered. With this approach to Mixture models: inference and applications to clustering. New York: Geoff McLachlan -Google Scholar Citations 1 Jan 1988 . Mixture models: inference and applications to clustering. Mindra Jaya. Added by. Mindra Jaya. Trending. Views Mixture model clustering using the MULTIMIX program frequent applications over recent years is due to the fact that mixture models offer natural models . Mixture Models. Inference and Applications to Clustering. Mixture Models: Inference and Applications to Clustering by Geoffrey . Booktopia has Mixture Models, Inference and Applications to Clustering by Geoffrey J. McLachlan. Buy a discounted Hardcover of Mixture Models online from Handbook of Research on Systems Biology Applications in Medicine -Google Books Result Mixture Models: Inference and Applications to Clustering (Statistics: Textbooks & Monographs) by McLachlan, Geoffrey J.; Basford, Kaye E. at AbeBooks.co.uk Mixture models : inference and applications to clustering -UQ eSpace Buy Mixture Models: Inference and Applications to Clustering by Geoffrey J. McLachlan % off + free shipping all over Indiafor author name's Mixture Models: Mixture model -Wikipedia, the free encyclopedia Inference and Applications to Clustering Mixture models : inference and applications to clustering /

The main aim of this three-wave 35-year follow-up study among Finnish employees (n = 532) was to investigate whether Sense of Coherence (SOC) is more stable among those with high SOC compared to those with low SOC, as hypothesized by Antonovsky [Antonovsky, A. (1987). Unraveling the mystery of health: how people manage stress and stay well. San Francisco, CA, US: Jossey-Bass], using two measurement points over 13 years. The participants were first studied in adolescence in 1961–1963, after which they responded to two postal questionnaires measuring SOC, first in 1985, and again in 1998. The results of the Factor Mixture Modeling identified two groups of individuals with different levels and mean changes of SOC: high-SOC (n = 344) and low-SOC (n = 188) individuals. The results supported Antonovsky’s hypothesis: SOC was more stable among high-SOC individuals (stability coefficient 0.57) than among low-SOC individuals (0.31). The results further supported the factor structure of the 11-item SOC scale. However, out of the five generalized resistance resources investigated in adolescence, only the level of home care was associated with SOC in adulthood.

Background-Antisocial personality disorder (ASPD), violent and criminal behavior, and drug abuse disorders share the common antecedent of early aggressive, disruptive behavior. In the 1985-1986 school year teachers implemented the Good Behavior Game (GBG), a classroom behavior management strategy targeting aggressive, disruptive behavior and socializing children to the student role. From first grade through middle school the developmental trajectories of 2,311 students from 19 Baltimore City Public Schools were examined. This article reports the GBG impact on these trajectories and ASPD and violent and criminal behavior by age 19-21 among the selected 768 students.

We propose a new spectral index, the Normalized Difference Fraction Index (NDFI), for enhanced detection of forest canopy damage caused by selective logging activities and associated forest fires. The NDFI synthesizes information from several component fraction images derived from spectral mixture models. Interpretation of the NDFI data is facilitated by a contextual classification algorithm (CCA) that enables accurate mapping of logging and fire-derived canopy damages. The CCA utilizes detected log landings, which are the spatial signature of selective logging, as starting locations for searching the NDFI image for canopy damage. This process separates canopy changes due to logging and associated forest fires from those caused by other natural disturbances. These methods were tested in the Sinop region, in the Southern Brazilian Amazon. Forest transect inventories, conducted along a gradient of degraded forests, were used to evaluated the performance of the NDFI. The NDFI was more sensitive to canopy damage than any individual fraction and is shown to have the potential for further sub-classification of degradation levels in forest environments. Map accuracy of forest canopy damage using the CCA classifier, assessed with aerial videography images, was 94%. The proposed NDFI-CCA classifier approach can be fully automated and, therefore, holds great promise as a forest monitoring tool in tropical forests. D

Downloadable! Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available ...

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.

In many long-term clinical trials subjects often experience a number of events all of which might serve as important endpoints for medical studies. The analysis of such multiple events can be beneficial from both medical and statistical perspectives. We review and discuss some recent developments in the area of Bayesian methodologies for the analysis of multiple event times data, focusing more on semiparametric models based on the intensity functions. The nonparametric part of such models is modeled as a realization of a stochastic process; while the parametric part, which may include a regression parameter and a parameter quantifying the heterogeneity (i.e., frailty) of a population, is assumed to have a parametric distribution with possibly unknown hyperparameters. Numerical computations required to obtain posterior estimates of the parameters are generally based on a class of sampling based methods, broadly known as the Markov Chain Monte Carlo (MCMC) algorithms. The methodologies discussed in this chapter are motivated by and aimed at analyzing some common types of multiple event times data that are often observed in medical studies. Bayesian Exploratory Data Analysis (EDA) methods and diagnostics for model selection and model assessment are considered for each of these cases.

Mixture designs and corresponding analysis techniques are of considerable importance in food science and industry. Mixture data are generally challenging to model, since the mixture restrictions leads to both exact and near collinearity. Scheffé found an excellent way to eliminate the exact collinearity, by using a certain reparameterization of the ordinary least squares (OLS) regression model. Near collinearities can be eliminated by, for instance, variable selection. Partial least squares (PLS) regression does not assume linearly independent variables and handles both exact and near collinearity by projecting onto a lower dimensional subspace. Lately also variable selection has been combined with PLS regression in order to get more parsimonious models. In the present study, models found by OLS and PLS regression, both combined with variable selection, are compared with regard to interpretation, response optimisation and prediction, for regular mixtures, mixture -process and crossed mixture data. Examples from sausages and hearth bread production are considered. D

Isobaric heat capacity of rocket propellant (RP-1 fuel) has been measured with a vacuum adiabatic calorimeter immersed in a precision liquid thermostat. Measurements were made in the temperature range from 293 to 671 K and at pressures up to 60 MPa. The uncertainty of heat capacity, pressure, and temperature measurements were estimated to be 2-2.5%, 0.05%, and 15 mK, respectively. The measured values of heat capacity were compared with the values calculated from a surrogate mixture model (equation of state, EOS). The average absolute deviation (AAD) between the present data and the values calculated with EOS was 0.81%. Ó

This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state-space models, Markov switching and mixture models, and models allowing for timevarying volatility. A final section reviews some recent approaches to nonparametric Bayesian modelling of time series.

The inverse Weibull distribution has the ability to model failure rates which are quite common in reliability and biological studies. A three-parameter generalized inverse Weibull distribution with decreasing and unimodal failure rate is introduced and studied. We provide a comprehensive treatment of the mathematical properties of the new distribution including expressions for the moment generating function and the rth generalized moment. The mixture model of two generalized inverse Weibull distributions is investigated. The identifiability property of the mixture model is demonstrated. For the first time, we propose a location-scale regression model based on the log-generalized inverse Weibull distribution for modeling lifetime data. In addition, we develop some diagnostic tools for sensitivity analysis. Two applications of real data are given to illustrate the potentiality of the proposed regression model.