An alternative estimator for multiple characteristics in PPS sampling

The problem considered relates to large-scale sample surveys. A new estimator of population total for the characteristics that are poorly correlated with the selection probabilities has been developed for the PPSWR sampling scheme. The relative efficiency of the proposed estimator has been studied under a super-population model. A numerical investigation into the performance of the estimator has also been made.     

Managing Demand Uncertainty through Core Acquisition in Remanufacturing

We discuss the optimal raw material acquisition strategy for a third party remanufacturer (3PR). We specifically investigate whether a 3PR should acquire used products or cores in bulk with uncertain quality levels, or in sorted grades with known quality levels; and whether to acquire and remanufacture cores before the demand is realized (planned acquisition), or after the demand is realized (reactive acquisition), or on both occasions (sequential acquisition). When only sorted cores are acquired, we find that, (i) it is optimal to acquire cores in multiple grades to balance acquisition and remanufacturing costs; (ii) if reactive acquisition is possible, it reduces the assortment size (number of grades in which cores are acquired) and the total inventory acquired in the planned acquisition; and (iii) the optimal portfolio of grades to acquire and the optimal acquisition and remanufacturing quantities of these grades can be determined analytically. When bulk cores are acquired in addition to sorted cores, the property of reduction in assortment size of the planned acquisition is preserved. We also show that the 3PR should acquire only a fraction of the demand in planned acquisition, and leave the rest for reactive acquisition. This fraction changes during the lifecycle of a remanufactured product. Using a combination of empirical and realistic data from a smartphone remanufacturer we show that sequential acquisition increases expected profit by up to 8% and 27% over only planned and only reactive acquisitions respectively, and reduces the inventory acquired by up to 21% over only planned acquisition.     

Human Capital Attainment and Gender Empowerment: The Kerala Paradox*

Objectives. The State of Kerala in southwestern India shows the highest gender development index in the country. Despite having the highest literacy rate and educational achievements of women, the suicide rate among females is very high, and domestic violence committed against women is increasing every year in Kerala. This article attempts to analyze the demographic, social, and cultural changes that are occurring in order to understand this apparent paradox of high human capital attainment and high violence and suicides experienced by women in Kerala. Methods. The study uses data from the Census of India (1991, 2001) , National Crime Records Bureau (1998–2000), and Reproductive and Child Health ( District Level House Hold Survey—2001 ) to analyze trends in crime, violence, and suicide rates, and demographic profiles of the different districts in Kerala. Results. High educational attainment has fostered new aspirations and attitudes among women in Kerala. Yet, societal and cultural norms still dictate that women should be subservient to men both at home and in the labor market. This imbalance often contributes to family violence and suicides in Kerala. Thus, beneath the veil of development lie some disturbing social, cultural, and economic issues that may be contributing to high rates of suicide and family violence in Kerala. Conclusions. This study demonstrates that high educational attainment alone will not promote gender empowerment unless the social and cultural fabric of a country or state ensures equality of women in all areas of life.     

An Application of EM Algorithm to a Change-Point Problem

We consider the problem of change-point in a classical framework while assuming a probability distribution for the change-point. An EM algorithm is proposed to estimate the distribution of the change-point. A change-point model for multiple profiles is also proposed, and EM algorithm is presented to estimate the model. Two examples of Illinois traffic data and Dow Jones Industrial Averages are used to demonstrate the proposed methods.     

Bayes Prediction for a Heteroscedastic Regression Superpopulation Model Using Balanced Loss Function

We consider Prais–Houthakker heteroscedastic normal regression model having variance of the dependent variable same as square of its expectation. Bayes predictors for the regression coefficient and the mean of a finite population are derived using Zellner's balanced loss function. Bayes predictive expected losses are obtained and compared with those of classical predictors and Bayes predictors under squared error loss function to examine their loss robustness.     

On the mixtures of Weibull and Pareto (IV) distribution: An alternative to Pareto distribution

Finite mixture models have provided a reasonable tool to model various types of observed phenomena, specially those which are random in nature. In this article, a finite mixture of Weibull and Pareto (IV) distribution is considered and studied. Some structural properties of the resulting model are discussed including estimation of the model parameters via expectation maximization (EM) algorithm. A real-life data application exhibits the fact that in certain situations, this mixture model might be a better alternative than the rival popular models.     

A qualitative study on the pros and cons of ethnically matching culturally and linguistically diverse (CALD) client families and child protection caseworkers

A review of the literature indicated that empirical research into the advantages and disadvantages of ethnically matching child protection caseworkers and culturally and linguistically diverse (CALD) client families is not extensive. The main aim of this study was to address this gap. Qualitative interviews were conducted with 29 CALD client families and 17 child protection caseworkers (as part of a larger study). It was found that if CALD families fear a breach of confidentiality (because of cultural reasons to protect the family's privacy and name), and this fear exceeds the two main benefits of ethnic matching – namely, that it helps overcome language barriers and provides (cultural) sensitivity – then they are likely to prefer a non-matched caseworker. This may be a challenge in child protection practice if the pool of culturally competent caseworkers is small. Another challenge identified was when CALD families preferred an ethnic match but was seen by case managers as a risk to the accurate assessment of a child's wellbeing because the caseworker was over-identifying with the client family. Thus, the provision of a culturally sensitive service can be at odds with the need to protect children from harm. This issue is seen as best addressed on a case by case basis. This study is significant because it has shown that ‘over-identification’ is an important work practice issue in this matter. It has also used empirical evidence to inform best practice principles regarding ethnic matching.     

Bayesian decision theoretic approach to hypothesis problems with skewed alternatives

Many hypothesis problems in practice require the selection of the left side or the right side alternative when the null is rejected. For parametric models, this problem can be stated as _H0:θ=_θ0$H_0:\theta ={\theta }_0$vs.  _H−:θ<_θ0$H_{-}:\theta <{\theta }_0$ or _H+:θ>_θ0$H_{+}:\theta >{\theta }_0$. Frequentists use Type-III error (directional error) to develop statistical methodologies. This approach and other approaches considered in the literature do not take into account the situations where the selection of one side may be more important or when one side may be more probable than the other. This problem can be tackled by specifying a loss function and/or by specifying a hierarchical prior structure with allowing the skewness in the alternatives. Based on this, we develop a Bayesian decision theoretic methodology and show that the resulted Bayes rule perform better in the side of the alternatives which is more probable. The methodology can be also used in a frequentist's framework when it is desired to discover an alternative that is more important. We also consider the multiple hypotheses problem and develop new false discovery rates for the selection of the left and the right sides of alternatives. These discovery rates would be useful in the situations when one side of the alternatives are more important or more probable than the other.     

A new count model generated from mixed Poisson transmuted exponential family with an application to health care data

In this article, a new mixed Poisson distribution is introduced. This new distribution is obtained by utilizing mixing process, with Poisson distribution as mixed distribution and Transmuted Exponential as mixing distribution. Distributional properties like unimodality, moments, over-dispersion, infinite divisibility are studied. Three methods viz. Method of moment, Method of moment and proportion, and Maximum-likelihood method are used for parameter estimation. Further, an actuarial application in context of aggregate claim distribution is presented. Finally, to show the applicability and superiority of proposed model, we discuss count data and count regression modeling and compare with some well established models.