Bootstrap Method

In this letter, we investigate the effect of station distribution (including the number and azimuthal gap of stations) upon location error based on the field data observed at Northern California Seismic Network (NCSN) using a double-difference earthquake location algorithm and a bootstrap method. The earthquakes relocated by all 117 stations are set as reference and the error of location is defined as the RMS of the difference to the reference. We find that the location error has a nonlinear relationship with the distribution of stations. The results may be used as guidelines for building seismic network.

Prenatal maternal stress has been linked to multiple adverse outcomes. Researchers have used a variety of methods to assess maternal stress. The purpose of this study was to explore and compare factors associated with stress in pregnancy as measured by perceived stress and stressful life events. We analyzed data from the Canadian Maternity Experiences Survey. A randomly selected sample of 8,542 women who had recently given birth was drawn from the 2006 Canadian Census. Women were eligible if they were at least 15 years of age, had delivered a live, singleton infant, and were living with their infant at the time of the interview (5-14 months postpartum). Prevalence estimates and odds ratios were calculated using sample weights of the survey and their variances were calculated using bootstrapping methods. Bivariate analyses identified statistically significant factors associated with each stress measure. Backward stepwise multivariate logistic regression models were constructed. A total of 6,421 women (78%) participated in the computer assisted telephone interview. Twelve percent of women experienced high levels of perceived stress and 17.1% reported having three or more stressful life events in the year prior to the birth of their baby. In the final model, psychosocial variables were associated with both outcomes, whereas demographic factors were associated only with life event stress. Different factors contributed to perceived stress and life event stress, suggesting that these concepts measure different aspects of stress. These findings can inform routine psychosocial risk assessment in pregnancy.

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

A class of weighted bootstrap techniques, called biased bootstrap or b-bootstrap methods, is introduced. It is motivated by the need to adjust empirical methods, such as the`uniform' bootstrap, in a surgical way to alter some of their features while leaving others unchanged. Depending on the nature of the adjustment, the b-bootstrap can be used to reduce bias, or to reduce variance or to render some characteristic equal to a predetermined quantity. Examples of the last application include a b-bootstrap approach to hypothesis testing in nonparametric contexts, where the b-bootstrap enables simulation`under the null hypothesis', even when the hypothesis is false, and a b-bootstrap competitor to Tibshirani's variance stabilization method. An example of the bias reduction application is adjustment of Nadaraya±Watson kernel estimators to make them competitive with local linear smoothing. Other applications include density estimation under constraints, outlier trimming, sensitivity analysis, skewness or kurtosis reduction and shrinkage.

Lo scopo del presente lavoro è quello di estendere al caso del campionamento per cattura-ricattura il metodo bootstrap per la stima della varianza di stimatori costruiti su campioni da popolazioni finite. Nel campionamento da popolazioni di animali, non è raro il caso in cui alcuni animali, già catturati una volta, mostrino una accresciuta familiarità nei confronti del contatto umano, mentre altri tendano a nascondersi. In questi casi, le probabilità di inclusione possono risultare modificate. In questo lavoro si presentano due applicazioni dell'algoritmo bootstrap per il campionamento PS π proposto da Mecatti (2000) adattate al caso del campionamento per cattura-ricattura. La prima riguarda la stima della varianza dell'usuale stimatore di Petersen della numerosità della popolazione. La seconda utilizza la stessa stima come numerosità delle popolazioni empiriche bootstrap su cui si basa l'algoritmo di Mecatti. Il lavoro si conclude con due simulazioni su dati reali.

In this paper we propose a new technique that uses the bootstrap to estimate con dence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging 1] and balancing 5]. Con dence and prediction intervals are estimated that include a signi cantly improved estimate of underlying model uncertainty (i.e.) the uncertainty of our estimate of the \true" regression. Unlike existing techniques, this estimate of uncertainty will vary according to which ensemble technique is used { if the e ect of using a speci c ensemble technique is to produce less model uncertainty than using another ensemble technique, then this will be re ected in the con dence and prediction intervals. Preliminary results illustrate how our technique can provide more accurate con dence and prediction intervals (intervals that better re ect the desired level of con dence (e.g.) 90%, 95%, etc.) for neural network ensembles than previous attempts.

Radiographic testing is a well-established non-destructive testing method to detect subsurface welding defects. In this paper, an automatic computer-aided identification system was implemented to recognize different types of welding defects in radiographic images. Imageprocessing techniques such as background subtraction and histogram thresholding were implemented to separate defects from the background. Twelve numeric features were extracted to represent each defect instance. The extracted feature values are subsequently used to classify welding flaws into different types by using two well-known classifiers: fuzzy k-nearest neighbor and multi-layer perceptron neural networks classifiers. Their performances are tested and compared using the bootstrap method. q

This study provides a first broad systematic treatment of the euagarics as they have recently emerged in phylogenetic systematics. The sample consists of 877 homobasidiomycete taxa and includes approximately one tenth (ca. 700 species) of the known number of species of gilled mushrooms that were traditionally classified in the order Agaricales. About 1000 nucleotide sequences at the 5′ end of the nuclear large ribosomal subunit gene (nLSU) were produced for each taxon. Phylogenetic analyses of nucleotide sequence data employed unequally weighted parsimony and bootstrap methods. Clades revealed by the analyses support the recognition of eight major groups of homobasidiomycetes that cut across traditional lines of classification, in agreement with other recent phylogenetic studies. Gilled fungi comprise the majority of species in the euagarics clade. However, the recognition of a monophyletic euagarics results in the exclusion from the clade of several groups of gilled fungi that have been traditionally classified in the Agaricales and necessitates the inclusion of several clavaroid, poroid, secotioid, gasteroid, and reduced forms that were traditionally classified in other basidiomycete orders. A total of 117 monophyletic groups (clades) of euagarics can be recognized on the basis on nLSU phylogeny. Though many clades correspond to traditional taxonomic groups, many do not. Newly discovered phylogenetic affinities include for instance relationships of the true puffballs (Lycoperdales) with Agaricaceae, of Panellus and the poroid fungi Dictyopanus and Favolaschia with Mycena, and of the reduced fungus Caripia with Gymnopus. Several clades are best supported by ecological, biochemical, or trophic habits rather than by morphological similarities.

... A TEST FOR IXDEPENDENCE BASED ON THE CORRELATION DIhIENSION ... The first order asymptotic distribution of tlie test statistic is independent of estimation error provided tliat the parameters of the model under test can be estimated 6-consistently. ...

Optimal alternative bivariate ranked set sample designs for the matched pairs sign test are obtained. Our investigation reviled that the optimal bivariate ranked set sample designs for matched pairs sign test are those with quantifying order statistics with labels , 2 distributions, asymptotic distributions and Pitman efficiencies of those designs are derived.

This paper employs the recently developed structural stability test with multiple regime shifts and grid bootstrapping methods to model US inflation dynamics over the past half century. Our empirical results suggest that the persistence of inflation has witnessed significant declines over the most recent period of low inflation and this helps to embed a low inflation environment. The finding is

Referendum style willingness to pay questions have been used to estimate passive use values. This referendum question format method may be problematic for many reasons, including the statistical techniques used to estimate willingness to pay from discrete responses. This paper compares a number of parametric, semi-nonparametric and nonparametric estimation techniques using data collected from US households regarding Federal protection of endangered fish species.The advantages and disadvantages of the various statistical models used are explored. A hypothesis test for statistical equality among estimation techniques is performed using a jackknife bootstrapping method. When the equality test is applied, the modeling techniques do show significant differences in some possible comparisons, but only those that are nonparamentric. This can lead to conflicting interpretations of what the data show. Resource managers and policy analysts need to use caution when interpreting results until an industry standard can be developed for estimating willingness to pay from closed ended questions.

In recent years, small businesses have received much attention from policy makers and researchers, in that these businesses are considered important for economic growth and job creation. At the same time small businesses are assumed to face major problems in securing long-term external finance, which is regarded as restraining their development and growth. Small business managers are assumed to use institutional finance as a means of meeting the need for resources, and as a consequence the major part of the research on small business finance has focused on constraints in the supply of institutional (market) finance.

Diffuse reflectance spectroscopy (DRS) is attracting much interest in the soil science community because it has a number of advantages over conventional methods of soil analyses. The techniques are more rapid, timely, cheaper and hence more efficient at obtaining the data when a large number of samples and analysis are required. Moreover, a single spectrum may be used to assess various physical, chemical and biological soil properties. Until now, research in soil spectroscopy has focused on spectral calibration and prediction of soil properties using multivariate statistics. In this paper we show how these predictions may be used in an inference system to predict other important and functional soil properties using pedotransfer functions (PTFs). Thus we propose the use of soil spectral calibration and its predictions as input and as a complement to a soil inference system (SPEC-SINFERS). We demonstrate the implementation of SPEC-SINFERS with two examples. As a first step, soil mid-infrared (MIR) spectra and partial least squares (PLS) regression are used to estimate soil pH, clay, silt, sand, organic carbon content and cation exchange capacity. A bootstrap method is used to determine the uncertainties of these predictions. These predictions and their uncertainties are then used as input into the inference system, where established PTFs are used to infer (i) soil water content and (ii) soil pH buffering capacity together with their uncertainties. An important feature of SPEC-SINFERS is the propagation of both input and model uncertainties.

We propose to model multivariate volatility processes based on the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high-dimensional optimization problem into several lower-dimensional subproblems. Consistency for the estimated CUCs has been established. A bootstrap method is proposed for testing the existence of CUCs. The proposed methodology is illustrated with both simulated and real data sets.

In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), the EM algorithm, and Multiple Imputation (MI) for analyzing multivariate normal data with missing responses. We also compare two approaches for modeling incomplete data (1) ignoring missing data and (2) joint modeling of response and non-response mechanisms. Several types of Software which can be used

In this paper we review recent developments on a bootstrap method for robust estimators which is computationally faster and more resistant to outliers than the classical bootstrap. This fast and robust bootstrap method is, under reasonable regularity conditions, asymptotically consistent. We describe the method in general and then consider its application to perform inference based on robust estimators for the linear regression and multivariate location-scatter models. In particular, we study confidence and prediction intervals and tests of hypotheses for linear regression models, inference for location-scatter parameters and principal components, and classification error estimation for discriminant analysis.

Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluation. It was a computer intensive method that can replace theoretical formulation with extensive use of computer. The Ordinary Least Squares (OLS) method often used to estimate the parameters of the regression models in the bootstrap procedure. Unfortunately, many statistics practitioners are not aware of the fact that the OLS method can be adversely affected by the existence of outliers. As an alternative, a robust method was put forward to overcome this problem. The existence of outliers in the original sample may create problem to the classical bootstrapping estimates. There was possibility that the bootstrap samples may contain more outliers than the original dataset, since the bootstrap re-sampling is with replacement. Consequently, the outliers will have an unduly effect on the classical bootstrap mean and standard deviation. Approach: In this study, we proposed to use a robust bootstrapping method which was less sensitive to outliers. In the robust bootstrapping procedure, we proposed to replace the classical bootstrap mean and standard deviation with robust location and robust scale estimates. A number of numerical examples were carried out to assess the performance of the proposed method. Results: The results suggested that the robust bootstrap method was more efficient than the classical bootstrap. Conclusion/Recommendations: In the presence of outliers in the dataset, we recommend using the robust bootstrap procedure as its estimates are more reliable.

Analysis of biological forms using landmark data has received substantial attention recently. Much of the statistical work in this area has concentrated on the estimation of average form, average form difference, and average growth difference. From the statistical, as well as the scientific point of view, it is important that any estimate of a scientifically relevant quantity be accompanied by a statement regarding its accuracy. Such a statement is contained in a confidence interval. The purpose of this paper is to provide a method to obtain confidence intervals for form difference and growth difference estimators. The estimators are based on Euclidean distance matrix analysis. The confidence intervals are calculated using the model independent bootstrap method. We illustrate the method by using three examples: morphological differences between samples of craniofacial patients and normal controls using two dimensional data from head X-rays, sexual dimorphism of craniofacial morphology in Cebus apella, and sexual dimorphism of facial growth in Cebus apella using three-dimensional data.

In recent years, the bootstrap method has been extended to time series analysis where the observations are serially correlated. Contributions have focused on the autoregressive model producing alternative resampling procedures. In contrast, apart from some empirical applications, very little attention has been paid to the possibility of extending the use of the bootstrap method to pure moving average (MA) or mixed ARMA models. In this paper, we present a new bootstrap procedure which can be applied to assess the distributional properties of the moving average parameters estimates obtained by a least square approach. We discuss the methodology and the limits of its usage. Finally, the performance of the bootstrap approach is compared with that of the competing alternative given by the Monte Carlo simulation.

We study the feasibility of producing relevant statistics to measure the impact of globalization in the Canadian economy. In this project we require means of key economic variables for the wholesale industry at the level of “Trade Group” by “Province” by “Globalization Indicators”. We consider small area estimation using area level models with random effects and use penalized splines in order to accommodate departures from linearity. We propose a new bootstrap method to estimate the mean squared errors of the small area estimators. We illustrate the methodology with data from a particular trade group.

In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation methods, cohort and duration (intensity), with 22 years of credit ratings data. We find that the bootstrapped intervals for the durationbased estimates are relatively tight when compared to either analytic or bootstrapped intervals around the less efficient cohort estimator. We show how the large differences between the point estimates and confidence intervals of these two estimators are consistent with non-Markovian migration behavior. Surprisingly, even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g. a PD AAÀ from a PD A+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated PDs. Conditioning on the state of the business cycle helps: it is easier to distinguish adjacent PDs in recessions than in expansions.

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always   times larger than its theoretic counterpart, where   with y as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return

This paper summarizes the present status of confidence interval estimation for one-factor random models. We provide both closed-form intervals and generalized confidence intervals for several parameters. Simple numerical examples are provided to illustrate computations. In order to limit the focus of this review, we consider only one-at-a-time (non-simultaneous) intervals with homoscedastic normal error terms, and do not include any Bayesian or bootstrap methods.

Polymorphisms in nitrosamine metabolism, DNA repair, and immune response genes have been associated with nasopharyngeal carcinoma (NPC). Studies have suggested chromosomal regions involved in NPC. To shed light on NPC etiology, we evaluated host gene expression patterns in 31 NPC and 10 normal nasopharyngeal tissue specimens using the Affymetrix Human Genome U133 Plus 2.0 Array. We focused on genes in five a priori biological pathways and chromosomal locations. Rates of differential expression within these prespecified lists and overall were tested using a bootstrap method. Differential expression was observed for 7.6% of probe sets overall. Elevations in rate of differential expression were observed within the DNA repair (13.7%; P = 0.01) and nitrosamine metabolism (17.5%; P = 0.04) pathways. Differentially expressed probe sets within the DNA repair pathway were consistently overexpressed (93%), with strong effects observed for PRKDC, PCNA, and CHEK1. Differentially expressed probe sets within the nitrosamine metabolism pathway were consistently underexpressed (100%), with strong effects observed for NQ01, CYP2B6, and CYP2E1. No significant evidence of increases in rate of differential expression was seen within the immune/inflammatory pathway. A significant elevation in rate of differential expression was noted for chromosome 4p15.1-4q12 (13.0%; P = 0.04); both overexpression and underexpression were evident (38% and 62%, respectively). An elevation in the rate of differential expression on chromosome 14q32 was observed (11.3%; P = 0.06) with a consistent pattern of gene underexpression (100%; P < 0.0001). These effects were similar when excluding late-stage tumors. Our results suggest that nitrosamine activation and DNA repair are important in NPC. The consistent down-regulation of expression on chromosome 14q32 suggests loss of heterozygosity in this region.

Meta-analysis is a statistical technique that allows one to combine the results from multiple studies to glean inferences on the overall importance of various phenomena. This method can prove to be more informative than common ''vote counting,'' in which the number of significant results is compared to the number with nonsignificant results to determine whether the phenomenon of interest is globally important. While the use of metaanalysis is widespread in medicine and the social sciences, only recently has it been applied to ecological questions. We compared the results of parametric confidence limits and homogeneity statistics commonly obtained through meta-analysis to those obtained from resampling methods to ascertain the robustness of standard meta-analytic techniques. We found that confidence limits based on bootstrapping methods were wider than standard confidence limits, implying that resampling estimates are more conservative. In addition, we found that significance tests based on homogeneity statistics differed occasionally from results of randomization tests, implying that inferences based solely on chi-square significance tests may lead to erroneous conclusions. We conclude that resampling methods should be incorporated in meta-analysis studies, to ensure proper evaluation of main effects in ecological studies.

In this survey study of 430 undergraduates, elements of the social competencies and interpersonal processes model were tested. Two social competencies were hypothesized to mediate the direct effects of 2 independent variables, attachment anxiety and avoidance, on 2 outcomes, psychological distress and perceived social support. Social self-efficacy was expected to be a significant mediator only for attachment anxiety. Emotional awareness, construed as low levels of alexithymia, was expected to be a significant mediator only for attachment avoidance. A bootstrap method was used to estimate the significance of indirect effects. Structural equation analyses suggested that, instead of specialized significant parings of one mediator with one independent variable, both social self-efficacy and emotional awareness served as significant mediators for both attachment anxiety and attachment avoidance.

The paper presents a Java framework for stochastic modelling and simulation, used as an infrastructure to model and simulate real world activities, phenomena and processes, particularly in health care, patient monitoring and medical education. We modelled the flow of patients through medical units, considering both their arrivals and their stay in the hospital. Also, we implemented bootstrapping methods, which are quite useful in simulation studies. We created bootstrapping e-tools for simulating laboratory works and experiments, to be used in both didactic and research activities.

This paper focuses on the problem of the estimation of a distribution on an arbitrary complete separable metric space when the data points are subject to censoring by a general class of random sets. If the censoring mechanism is either totally observable or totally ordered, a reverse probability estimator may be defined in this very general framework. Functional central limit theorems are proven for the estimator when the underlying space is Euclidean. Applications are discussed, and the validity of bootstrap methods is established in each case.

This paper explores a relatively new methodology, the directional distance function method, to analyse productivity growth. The method allows us to explicitly evaluate the role that undesirable outputs of the economy, such as carbon dioxide and other greenhouse gases, have on the frontier production process which we specify as a piecewise linear and convex boundary function. We decompose productivity growth into efficiency change (catching up) and technology change (innovation). We test the statistical significance of the estimates using recently developed bootstrap methods. We also explore implications for growth of total factor productivity in the OECD and Asian economies.

Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful because they detect that the sampling distribution of the mediated effect is skewed away from 0. They argue that R. M. recommendation of first testing the X → Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility. Empirical examples and computer setups for bootstrap analyses are provided.

Theories applied to work stress predict that coping will mediate and support will moderate the impact of work demands on worker well-being. We explored the mediating and moderating effects of coping and support on the relationship between perceived work demands and burnout in support staff working with adults with intellectual disabilities. Ninety-six support staff completed questionnaires that measured demographic factors, perceived work demands, coping, support, and burnout. A sub-sample participated in a follow-up 22 months later. Cross-sectional regression analyses revealed a relationship between work demands and emotional exhaustion burnout that reduced when wishful thinking coping was introduced as a predictor. Exploration of multiple mediator effects using bootstrap methods revealed that wishful thinking partially mediated the relationship between work demands and emotional exhaustion but practical coping did not. Practical coping had a main effect relationship with personal accomplishment, and there was evidence that support moderated the impact of work demands on personal accomplishment (although not fully consistent with theory). Study variables, other than personal accomplishment, were stable over 22 months but no longitudinal relationships between coping and burnout was found. These findings emphasise the importance of coping in managing work demands and for the development of burnout in support staff.

Several studies that have investigated a few stocks have found that the spacing between consecutive financial transactions (referred to as trade duration) tend to exhibit long-range dependence, heavy tailedness, and clustering. In this study, we empirically investigate whether a larger sample of stocks exhibit those characteristics. Comparing goodness of fit in modeling trade duration data for stable distribution and fractional stable noise based on a procedure applying bootstrap methods developed by the authors, the empirical results in this paper suggest that the fractional stable noise and stable distribution dominate alternative distributional assumptions (i.e., lognormal distribution, fractional Gaussian noise, exponential distribution, and Weibull distribution) in our study.

The finite annual rate of population increase (l) is a fundamental demographic parameter that characterizes the relative annual change in animal numbers. Uncertainty in the estimation of l from demographic population viability analyses (PVAs) has been largely limited to sensitivity analysis, calculating a pseudo-distribution for b l using Monte Carlo methods, or by use of bootstrap methods. The delta method has been used and suggested by several researchers, but no one has provided the computational means to implement it. In this paper, we present Mathematica code to calculate l and its variance based on eigenvalue calculations of a Leslie transition matrix. We demonstrate the procedure using data from a Hawaiian hawk (Buteo solitarius) study.

This paper uses data envelopment analysis (DEA) to estimate the technical efficiency of 12 hotels in Luanda, Angola. The study uses a balanced data set with 84 observations over the years 2000-2006. In a second stage, this paper uses a bootstrapping method (rather than a Tobit regression) developed by Simar and Wilson [(2007). Estimation and inference in two stage, semi-parametric models of productive efficiency. Journal of Econometrics, 136, 31-64] to estimate the economic drivers behind the technical efficiency. They find that efficiency has risen over the studied period, but at a decreasing rate. Secondly, they find that a hotel's membership in a group increases efficiency. Greater market share as measured by a Herfindahl index, increases efficiency and finally, hotels with an international strategy have higher efficiencies. r

Effects of a Cognitive Behavioral Stress Management Intervention on the Psychological, Endocrinological, and Immunological Health of Minority Women Co-infected with HIV and HPV. Abstract of a thesis at the University of Miami. Thesis supervised by Professor Michael H. Antoni. No. of pages in text (86) Women infected with HIV are at an increased risk for infection of Human Papillomavirus (HPV), developing cervical lesions, and developing cervical cancer. Prior research has suggested disruptions in the immune system as well as circulating levels of stress and gonadal hormones as possible explanations for the increase of HPV infection in women with HIV. Additionally, psychosocial factors such as symptoms of depression and distress have also been associated with HPV infection, as well as disruptions in immune and endocrinologial systems, suggesting a psychoneuroimmunological pathway to disease progression. It was hypothesized that HIV+HPV+ women assigned to a Cognitive Behavioral Stress Management (CBSM) intervention will experience improvements in disease status, immune markers, circulating stress hormones, and reductions of depression and distress symptoms. An exploratory investigation of the effects of CBSM in levels circulating reproductive hormones was also tested. Follow-up hypotheses tested whether CBSM effects on immune variables were explained by reductions in symptoms of depression, distress, NE, cortisol, and increases of DHEA-S. Additionally, it was hypothesized that CBSM effects on stress hormones would be mediated by reductions in distress and depression symptoms. Finally, it was hypothesized that improvements in immune parameters would be correlated with decreases in risk of cervical dysplasia at a 9 month follow-up. Participants were 71 women co-infected with HIV and HPV that were mostly of African American, Haitian, Latina, and Caribbean descent. Hierarchical regression analyses were performed and showed a significant CBSM effect in decreases on BDI somatic depression subscale scores and increases in NK cell counts. Additionally, there was a marginally significant effect of CBSM on increases in CD4+ T-cells and decreases in urinary NE output. The bootstrapping method evidenced a mediation model, where the relationship between group assignment and CD4+ cell counts was explained by lower BDI somatic scores. More research is necessary to fully elucidate the psychobiological trajectories of disease as immunological changes in our sample did not explain the reduced odds of dysplasia in the women assigned to the CBSM group.