Variance Analysis

Semiflexible pavement (SFP), a composite pavement, is formed by filling into a very open porous asphalt skeleton a specifically designed water consistency fluid mortar with a very high early and 28-day strength. The amalgamation of both components will produce a SFP where it will replace the conventional wearing course. The main goal of this investigation is to study the effect of various aggregate gradations towards producing SFP. These include determining the optimum binder, volumetric properties, and durability and strength of each aggregate gradation. The final results were statistically analyzed and two factor variance analyses (ANOVA) were performed to check on the significance at certain confidence limits. The results confirmed that different aggregate gradations significantly affect the properties mentioned.

Most of the statistical procedures in meta-analysis are based on the estimation of average effect sizes from a set of primary studies. The optimal weight for averaging a set of independent effect sizes is the inverse variance of each effect size, but in practice these weights have to be estimated, being affected by sampling error. When assuming a random-effects model, there are two alternative procedures for averaging independent effect sizes: Hunter and Schmidt’s estimator, which consists of weighting by sample size as an approximation to the optimal weights; and Hedges and Vevea’s estimator, which consists of weighting by an estimation of the inverse variance of each effect size. In this article, the bias and mean squared error of the two estimators were assessed via Monte Carlo simulation of meta-analyses with the standardized mean difference as the effect-size index. Hedges and Vevea’s estimator, although slightly biased, achieved the best performance in terms of the mean square...

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

Optimization of process parameters in sheet metal forming is an important task to reduce manufacturing cost. To determine the optimum values of the process parameters, it is essential to find their influence on the deformation behaviour of the sheet metal. The significance of three important process parameters namely, die radius, blank holder force and friction coefficient on the deep-drawing characteristics of a stainless steel axi-symmetric cup was determined. Finite element method combined with Taguchi technique form a refined predictive tool to determine the influence of forming process parameters. The Taguchi method was employed to identify the relative influence of each process parameter considered in this study. A reduced set of finite element simulations were carried out as per the Taguchi orthogonal array. Based on the predicted thickness distribution of the deep drawn circular cup and analysis of variance test, it is evident that die radius has the greatest influence on the deep drawing of stainless steel blank sheet followed by the blank holder force and the friction coefficient. Further, it is shown that a blank holder force application and local lubrication scheme improved the quality of the formed part.

Currently, polymer modified asphalt mixture is a relatively costly mixture for paving roads. One way to reduce the cost of such constructions and rendering them more convenient is by using inexpensive polymers, i.e. waste polymers. The main purpose of this research is to determine the effect of incorporating waste plastic bottles (Polyethylene Terephthalate (PET)) on the engineering properties of stone mastic asphalt (SMA) mixture. The volumetric and mechanical properties of asphalt mixes that include various percentages of PET (0%, 2%, 4%, 6%, 8% and 10%) were calculated and assessed with laboratory tests. The appropriate amount of PET was found to be 6% by weight of bitumen. The outcomes were statistically analysed and the determination of the significance at certain confidence limits was performed with the two factor variance analysis (ANOVA). Moreover, some studies conducted on polyethylene modified asphalt mixture have also been taken into consideration in this paper. The results show that the addition of PET has a significant positive effect on the properties of SMA and it can promote the re-use of waste material in industry in an environmentally friendly and economical way.► The PET increased the stiffness level of the mixture improving its resistance level against permanent deformation. ► The effects of waste PET on Marshall Stability, air void and bulk specific gravity of the mixture are significant. ► The appropriate amount of PET was found to be 6% by weight of bitumen.

Semiflexible pavement (SFP), a composite pavement, is formed by filling into a very open porous asphalt skeleton a specifically designed water consistency fluid mortar with a very high early and 28-day strength. The amalgamation of both components will produce a SFP where it will replace the conventional wearing course. The main goal of this investigation is to study the effect of various aggregate gradations towards producing SFP. These include determining the optimum binder, volumetric properties, and durability and strength of each aggregate gradation. The final results were statistically analyzed and two factor variance analyses (ANOVA) were performed to check on the significance at certain confidence limits. The results confirmed that different aggregate gradations significantly affect the properties mentioned. © 2014 Nadiah Md. Husain et al.

http://www.hindawi.com/journals/amse/2014/529305/abs/

Currently, polymer modified asphalt mixture is a relatively costly mixture for paving roads. One way to reduce the cost of such constructions and rendering them more convenient is by using inexpensive polymers, i.e. waste polymers. The main purpose of this research is to determine the effect of incorporating waste plastic bottles (Polyethylene Terephthalate (PET)) on the engineering properties of stone mastic asphalt (SMA) mixture. The volumetric and mechanical properties of asphalt mixes that include various percentages of PET (0%, 2%, 4%, 6%, 8% and 10%) were calculated and assessed with laboratory tests. The appropriate amount of PET was found to be 6% by weight of bitumen. The outcomes were statistically analysed and the determination of the significance at certain confidence limits was performed with the two factor variance analysis (ANOVA). Moreover, some studies conducted on polyethylene modified asphalt mixture have also been taken into consideration in this paper. The results show that the addition of PET has a significant positive effect on the properties of SMA and it can promote the re-use of waste material in industry in an environmentally friendly and economical way. © 2011 Elsevier Ltd.

http://www.sciencedirect.com/science/article/pii/S0261306911004225

Summary.  The evaluation of results from mixtures of deoxyribonucleic acid (DNA) from two or more people in crime case investigations may be improved by taking not only the qualitative but also the quantitative part of the results into consideration. We present a statistical likelihood approach to assess the probability of observed peak heights and peak areas information for a pair of profiles matching the DNA mixture. Furthermore, we demonstrate how to incorporate this probability in the evaluation of the weight of the evidence by a likelihood ratio approach. Our model is based on a multivariate normal distribution of peak areas for assessing the weight of the evidence. On the basis of data from analyses of controlled experiments with mixed DNA samples, we exploited the linear relationship between peak heights and peak areas, and the linear relationships of the means and variances of the measurements. Furthermore, the contribution from one individual's allele to the mean area of this allele is assumed to be proportional to the average of peak height measurements of alleles, where the individual is the only contributor. For shared alleles in mixed DNA samples, it is possible to observe only the cumulative peak heights and areas. Complying with this latent structure, we used the EM algorithm to impute the missing variables on the basis of a compound symmetry model. The measurements were subject to intralocus and interlocus correlations not depending on the actual alleles of the DNA profiles. Owing to factorization of the likelihood, properties of the normal distribution and use of auxiliary variables, an ordinary implementation of the EM algorithm solved the missing data problem.

We consider the problem of maximum likelihood estimation of the parameters of the Pareto Type II (Lomax) distribution. We show that in certain parametrization and after modification of the parameter space to include exponential distribution as a special case, the MLEs of parameters always exist. Moreover, the MLEs have a non standard asymptotic distribution in the exponential case due to the lack of regularity. Further, we develop a likelihood ratio test for exponentiality versus Pareto II distribution. We emphasize that this problem is non standard, and the limiting null distribution of the deviance statistic in not chi-square. We derive relevant asymptotic theory as well as a convenient computational formula for the critical values for the test. An empirical power study and power comparisons with other tests are also provided. A problem from climatology involving precipitation data from hundreds of meteorological stations across North America provides a motivation for and an illustration of the new test.

Geometric quantiles are investigated using data collected from a complex survey. Geometric quantiles are an extension of univariate quantiles in a multivariate set-up that uses the geometry of multivariate data clouds. A very important application of geometric quantiles is the detection of outliers in multivariate data by means of quantile contours. A design-based estimator of geometric quantiles is constructed and used to compute quantile contours in order to detect outliers in both multivariate data and survey sampling set-ups. An algorithm for computing geometric quantile estimates is also developed. Under broad assumptions, the asymptotic variance of the quantile estimator is derived and a consistent variance estimator is proposed. Theoretical results are illustrated with simulated and real data.

Abstract Urbanization and demands for mobility have spurred the development of mass rapid transit infrastructure in industrializing Asia. Differences between the character of pre-existing urban structure in these localities and worldwide precedents suggest a need for studies ...

Within beds of blue mussel (Mytilus edulis L.), individuals are aggregated into small patches, which in turn are incorporated into bigger patches, revealing a complex hierarchy of spatial structure. The present study was done to find the different scales of variation in the distribution of mussel biomass, and to describe the spatial heterogeneity on these scales. The three approaches compared for this purpose were fractal analysis, spatial autocorrelation and hierarchical (or nested) analysis of variances (ANOVA). The complexity (i.e. patchiness) of mussel aggregations was described with fractal dimension, calculated with the semivariogram method. Three intertidal mussel beds were studied on the west coast of Sweden. The distribution of wet biomass was studied along transects up to 128 m. The average biomasses of blue mussels on the three mussel beds were 1825±210, 179±21 and 576±66 g per0.1 m2, respectively, and the fractal dimensions of the mussel distribution were 1.726±0.010, 1.842±0.014 and 1.939±0.029 on transects 1–3, respectively. Distributions of mussels revealed multiscaling behaviour. The fractal dimension significantly changed twice on different scales on the first bed (thus showing three scaling regions), the second and third beds revealed two and three scaling regions, respectively. High fractal dimension was followed by significant spatial autocorrelation on smaller scales. The fractal analysis detects the multiple scaling regions of spatial variance even when the spatial structure may not be distinguished significantly by conventional statistical inference. The study shows that the fractal analysis, the spatial autocorrelation analysis and the hierarchical ANOVA give complementary information about the spatial variability in mussel populations.

A family of density functions is considered which contains several life-testing models as specific cases. Uniformly minimum variance unbiased estimators are obtained for the positive and negative powers of the parameter, moments and reliability function. These general results provide the estimators for the specific models.