A Novel Asymmetric Distribution with Power Tails

In this article, we propose a four-parameter asymmetric doubly Pareto-uniform (DPU) distribution with support (?∞, ∞) whose density and cumulative distribution functions are constructed by seamlessly concatenating the left and right Pareto tails with a uniform central part. Properties of the distribution are described and a maximum likelihood estimation (MLE) procedure for its parameters is obtained. Two illustrative examples of the MLE procedure are provided. The first example utilizes an i.i.d. sample of standardized log-differences of bi-monthly 30-year U.S. conventional mortgage interest rates (1971–2004). The second example deals with the height of 100 female Australian athletes.     

A study on the role of interviewer in surveys

Non-sampling errors have many different sources, can occur at different stages of the survey and are a result of error contributions of different personnel or agencies Involved in executing the survey. In Interview based surveys,interviewer effectively influences both the response error as well as the response rate. In the present investigation an estimation procedure has been proposed to study the contribution of variance due to response errors as well as non-response errors introduced because of the interviewer.     

Likelihood-based Estimation of the Regression Model with Scrambled Responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator out-performs the classical estimators in all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.     

Almost unbiased ratio and product estimators based on interpenetrating subsamples

This paper proposes two classes of almost unbiased ratio and product estimators, in the case of interpenetrating subsample designs, which include the estimators considered by earlier authors. Several other almost unbiased ratio and product estimatbrs are given whlch are particular members of the proposed classes of estimators. Optimum estimators are also identified in these classes.     

Estimates of the parameters of type-II extreme value distribution from censored samples

Maximum likelihood estimators of a Type-II extreme value distribution are derived from doubly censored samples. The asymptotic variances and covariances of the maximum likelihood estimators are discussed and these are numerically evaluated for different censoring proportions q₁ = 0.0(0. l) (0.9) from below and q₂ = 0.0 (0. l) (0.9- q₁) from above. The asymptotic relative efficiencies of the parameter estimates revealed that lower order statistics are more important for estimating the parameters of Type-II extreme value distribution as compared to higher order statistics.     

The Effect of Family Background on Individual Wages and an Examination of Inequality of Opportunity in India

The paper attempts to explore the role of family background in contributing to the overall economic inequality in Indian society. Drawing on the recent work on equality of opportunity, and using the Indian Human Development Survey (IHDS, 2004–05), paper finds that the opportunity share of inequality in wage earnings in India ranges from 11% to 17% across different age based cohorts. Given the conservative view of what constitutes responsibility, the analysis is likely to identify only the lower bound of opportunity inequality. The paper also calls for redistributive policies favoring those who suffer from inferior circumstances.     

A NOTE ON VARIANCE ESTIMATION FOR THE GENERALIZED REGRESSION PREDICTOR

The generalized regression (GREG) predictor is used for estimating a finite population total when the study variable is well‐related to the auxiliary variable. In 1997, Chaudhuri & Roy provided an optimal estimator for the variance of the GREG predictor within a class of non‐homogeneous quadratic estimators (H) under a certain superpopulation model M. They also found an inequality concerning the expected variances of the estimators of the variance of the GREG predictor belonging to the class H under the model M. This paper shows that the derivation of the optimal estimator and relevant inequality, presented by Chaudhuri & Roy, are incorrect.     

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.     

A modified estimator of population mean using power transformation

Summary In this paper we have suggested two modified estimators of population mean using power transformation. It has been shown that the modified estimators are more efficient than the sample mean estimator, usual ratio estimator, Sisodia and Dwivedi’s (1981) estimator and Upadhyaya and Singh’s (1999) estimator at their optimum conditions. Empirical illustrations are also given for examining the merits of the proposed estimators. Following Kadilar and Cingi (2003) the work has been extended to stratified random sampling, and the same data set has been studied to examine the performance in stratified random sampling.