Moving up the energy ladder: does socio-religious status matter?

This paper investigates the role played by socio-religious categories in determining primary cooking fuel choices among Indian households. We study this role in the broader context of climbing up the energy ladder. Our estimates based on a sample of 601,509 households and using multinomial probit regression suggest that socio-religious status along with economic status is critical in the choice of modern eco-friendly fuels. We find that belonging to a marginalized community in Hindu religion significantly dampens a households’ likelihood to move up the ladder when compared with upper caste households. While intra-religion differences among Hindu castes in terms of their probability of using modern fuels are wider, differences among Muslims appear smaller. Also, though Muslims perform worse than Hindu upper castes in terms of probability of using modern fuels they are much better off in comparison with other Hindu castes. Our results remain robust to alternative specifications and several robustness checks.

     

Uniformity pattern and related criteria for q-level factorials

The objective of this paper is to study the issue of the projection discrepancy along the line of Liu (2002) and Fang and Qin (2005) based on discrete discrepancy measure proposed in Qin and Fang (2004), which has wide application to the field of fractional factorials. Here we also study the projection properties for q-level factorials and provide connection between minimum projection uniformity and other optimality criteria. A lower bound to projection discrepancy for q-level factorials is presented here.     

When Battlefields become Marketplaces: Migrant Workers and the Role of Civil Society and NGO Activism in Thailand

The vast majority of migrant workers in Thailand are employed predominantly in low‐paying occupations commonly described as “3‐D jobs” (dangerous, dirty, and difficult). Currently, there are nearly two million documented and undocumented migrant workers, mostly from neighbouring Burma, Lao People’s Democratic Republic, and Cambodia, employed in various industries, including domestic service, throughout the country. While over half a million migrants are officially registered to work in the country, both documented and undocumented migrant workers remain unprotected primarily due to the lack of concrete measures to monitor, implement and enforce laws regarding working and living conditions. Regardless of where they are employed, migrant workers face common problems: low wages; harmful working conditions, poor living conditions; discrimination and harassment, the threat of arrest and deportation; and lack of access to basic resources such as medical care and legal assistance. Based on preliminary research conducted in the summer of 2005, this article looks at the situation of migrant factory and domestic workers in Thailand and explores the ways in which local activists, NGOs, community‐based organisations, and international bodies have been looking to assist and protect migrant workers. Successful migrant workers’ struggles and ongoing efforts of mobilization have been made possible with the help of these support groups, and raise the possibility that union and NGO activity have the potential to improve the situation of migrants in Thailand. This also raises the question of whether advocacy groups should be acting in lieu of the state rather than alongside the state, especially when it appears that they are fulfilling their civic duty as enforcer and monitor of migrant workers’ problems.     

A Short Note on Almost Sure Convergence of Bayes Factors in the General Set-Up

ABSTRACT

Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups considered are usually specialized and often involves independent and identically distributed data. Even in such specialized cases, mostly weak consistency results are available. In this article, for the first time ever, we derive the almost sure convergence theory of Bayes factor in the general set-up that includes even dependent data and misspecified models. Somewhat surprisingly, the key to the proof of such a general theory is a simple application of a result of Shalizi to a well-known identity satisfied by the Bayes factor. Supplementary materials for this article are available online.     

Estimation of population mean in successive sampling under super-population model in presence of random non response situations

The present work is an attempt to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling in presence of random non response situations. The estimation strategy has been constructed under a super-population model design approach with the help of imputation technique. The estimators proposed on the current occasion cover the cases of occurrences random non responses on either of the occasions. Detail behaviors of the proposed class of estimators have been studied and its performance has been examined with the sample mean estimator. The results are demonstrated through empirical studies which establish the effectiveness of the proposed class of estimators. Suitable recommendations have been put forward to the survey statisticians for its practical application.     

On quantiles estimation based on different stratified sampling with optimal allocation

This work considers the problem of estimating a quantile function based on different stratified sampling mechanism. First, we develop an estimate for population quantiles based on stratified simple random sampling (SSRS) and extend the discussion for stratified ranked set sampling (SRSS). Furthermore, the asymptotic behavior of the proposed estimators are presented. In addition, we derive an analytical expression for the optimal allocation under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. The efficiency of the proposed estimates is further illustrated by analyzing a real data set from CHNS.     

A Class of Estimators of Population Variance Using Auxiliary Information in Sample Surveys

This article advocates the problem of estimating the population variance of the study variable using information on certain known parameters of an auxiliary variable. A class of estimators for population variance using information on an auxiliary variable has been defined. In addition to many estimators, usual unbiased estimator, Isaki's (1983), Upadhyaya and Singh's (1999), and Kadilar and Cingi's (2006) estimators are shown as members of the proposed class of estimators. Asymptotic expressions for bias and mean square error of the proposed class of estimators have been obtained. An empirical study has been carried out to judge the performance of the various estimators of population variance generated from the proposed class of estimators over usual unbiased estimator, Isaki's (1983), Upadhyaya and Singh's (1999) and Kadilar and Cingi's (2006) estimators.     

On the analysis of unbalanced two-level supersaturated designs via generalized linear models

Supersaturated designs (SSDs) are factorial designs in which the number of experimental runs is smaller than the number of parameters to be estimated in the model. While most of the literature on SSDs has focused on balanced designs, the construction and analysis of unbalanced designs has not been developed to a great extent. Recent studies discuss the possible advantages of relaxing the balance requirement in construction or data analysis of SSDs, and that unbalanced designs compare favorably to balanced designs for several optimality criteria and for the way in which the data are analyzed. Moreover, the effect analysis framework of unbalanced SSDs until now is restricted to the central assumption that experimental data come from a linear model. In this article, we consider unbalanced SSDs for data analysis under the assumption of generalized linear models (GLMs), revealing that unbalanced SSDs perform well despite the unbalance property. The examination of Type I and Type II error rates through an extensive simulation study indicates that the proposed method works satisfactorily.     

USING L2 ESTIMATION FOR L1 ESTIMATORS: AN APPLICATION TO THE SINGLE-INDEX MODEL

The bootstrap method is used to compute the standard error of regression parameters when the data are non-Gaussian distributed. Simulation results with L₁ and L₂ norms for various degrees of “non-Gaussianess” are provided. The computationally efficient L₂ norm, based on the bootstrap method, provides a good approximation to the L₁ norm. The methodology is illustrated with daily security return data. The results show that decisions can be reversed when the ordinary least-squares estimate of standard errors is used with non-Gaussian data.