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This paper considers the problem of estimating the population total of the sensitive variable under a unified approach. Odumade and Singh (2008) have suggested a forced quantitative randomized response model and then suggested an unbiased estimator of the population total Y of the sensitive variable y. It is observed that the optimum estimator of the population total Y due to Odumade and Singh (2008) depends on the unknown population parameter under investigation which lacks the practical utility. To overcome this drawback Odumade and Singh (2008) suggested another unbiased estimator of the population total of the sensitive variable based on two independent samples which causes the increase in the cost. Keeping this in view we define an alternative randomized response procedure and hence the estimator of the population total of the sensitive variable based on one sample only which is free from such limitation and more efficient than the Odumade and Singh (2008) estimator. The relative efficiency of the proposed estimator with respect to Bar-Lev et al. (2004) and Odumade and Singh (2008) estimators are examined through numerical illustration. [ABSTRACT FROM AUTHOR]

This paper suggests an efficient randomized response model. We have studied the properties of the suggested randomized response model and compared with Gjestvang and Singh (2009) and Tarray and Singh (2015) model. We have shown theoretically and empirically that the proposed model is more efficient than the Gjestvang and Singh (2009) and Tarray and Singh (2015) randomized response models. [ABSTRACT FROM AUTHOR]

In this article, some new randomized response models have been proposed. Properties of the proposed randomized response models have been studied. The proposed models are found to be more efficient than the randomized response models studied by Himmelfarb and Edgell [1], Gjestvang and Singh [2] and Singh [3] under certain realistic conditions. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

The crux of this paper is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Singh et al. [1] randomization device by utilizing the Poisson distribution in stratified sampling. This study also deals with the extension of the estimation reported by Singh and Tarray [2] using a Poisson distribution and an unrelated question randomized response model reported in Singh et al. [1]. In stratified sampling, the estimators are proposed when the parameter of the rare unrelated attribute is known and also when it is unknown. It is shown that the proposed models are more efficient than the model given by Lee et al. [3] in both cases, that is, when the proportion of persons possessing a rare unrelated attribute is known and that when it is unknown. When the sizes of the stratified populations are not given, other estimators are suggested using stratified double sampling. Properties of the proposed randomized response model are studied and recommendations are made. [ABSTRACT FROM AUTHOR]

Ranked set sampling is applicable whenever ranking of a set of sampling units can be done easily by a judgement method or based on the measurement of an auxiliary variable on the units selected. In this paper, we have suggested some linear shrinkage estimators of the scale parameter of Morgenstern type bivariate logistic distribution (MTBLD) using ranked set sampling alongwith their properties. Relevant conditions are obtained under which the suggested estimators are better than Chacko and Thomas [6] estimators and the minimum mean squared error (MMSE) estimators. Numerical illustrations are given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population proportion π_{S} possessing the sensitive attribute using stratified random sampling. Two alternatives to Kim and Warde [12] stratified randomized response model have been proposed. The proposed models are based on extending Singh [28] models to the case of stratified random sampling. It has been shown that the proposed models are more efficient than Kim and Warde [12] stratified randomized response technique under some realistic conditions in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. A practical problem with the use of optimum (Neyman) allocation has been pointed out. Thus, in practice, the use of either proportional allocation or equal allocation has been suggested while estimating proportion of a sensitive attribute using stratified randomized response sampling. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

Starting from the Singh et al. [28] [Current Science 57: 1331-1334] difference estimator, we define a class of estimators for the unknown variance of a survey variable on the line of Diana et al. [7] [Stat Methods Appl DOI 10.1007/s10260-010-0156-6] when auxiliary variable is available. The bias and mean square error of the estimators belonging to the class of estimators are derived and the expressions for the optimum parameters minimizing the asymptotic mean square error are given in closed form. A simple condition allowing us to improve the usual difference estimator due to Das and Tripathi [6] [Sankhya C 40: 139-148] is derived. Finally, in order to judge the merits of some estimators with the usual difference one, an empirical study is carried out. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population mean &Ymacr; of the study variate Y using information on the auxiliary variable X. A family of ratio-type and product-type estimators for the population mean &Ymacr; of the study variable y is defined by using the suitable transformation on the study variable and the auxiliary variable. Expressions of bias and variance of the suggested class of estimators are obtained up to the first order of approximation. Several known estimators available in the literature are identified as members of the proposed family of estimators. Numerical illustration is given in the support of the present study. [ABSTRACT FROM AUTHOR]

In general, failure and repair times for systems and components are thought of as being random in nature and therefore unpredictable. Even though these lifetimes are random, they can be modeled through the use of probability density functions. This paper studied the problem of Bayesian estimation of the mean time to failure (MTTF) when the life times are exponentially distributed. Exponential distribution yields Bessel function model in life testing. Censoring is a distinguishing feature of the field of survival analysis and in this paper we consider Type - II censoring that implies the life tests are terminated after pre-assigned number of failures has been considered. Bayes estimator of the average life has been obtained under LINEX (LINearly-EXponential) loss function. We also obtain corresponding Bayes estimators under squared error loss function (SELF) propounded by Bhattacharya [5] and compare the results from the two loss functions. We propose avenues for extensions of these results for Weibull life testing model. [ABSTRACT FROM AUTHOR]

In this paper, we have suggested a family of estimators &picirc;_W for estimating π, the proportion of population having sensitive category, under unrelated question randomized response model. It is shown that the suggested class of estimators &picirc;_W of π is more efficient than Mangat et al.'s [8] estimator &picirc;_1. Since &picirc;_W bases on the unknown scalar W, it has little practical utility. By replacing W in &picirc;_W by its various estimated values, tww estimators &picirc;_W^{(i)}, i = 1, 2, have been suggested for use in practice. Exact efficiency of &picirc;_W^{(i)} have been worked out numerically. [ABSTRACT FROM AUTHOR]

In this paper we have investigated some classes of shrinkage estimators for estimating dispersion parameter σ in p-dimensional analogue of the probable error of a single variate. The need to study this parameter arises due to its importance in target analysis problems to estimate Circular Probable Error (CPE) and Spherical Probable Error (SPE). It is assumed that the prior information or guessed value of the parameter σ say σ_0 is available from the past experiences. The properties of the developed shrinkage estimators have been studied when center of impact is known and when it is unknown by using appropriate numerical integration method (10-point Gauss-Laguerre integration method). Simulation studies confirm the high efficiency of the developed classes of shrunken estimators when compared with the usual maximum likelihood estimators (MLE), unbiased estimators and minimum mean squared error (MMSE) estimators. [ABSTRACT FROM AUTHOR]

The present paper investigates some classes of shrinkage estimators for estimating standard deviation in the case of univariate normal parent when some a priori or guessed interval containing this parameter is available from past experience. The need to study this parameter arises owing to its importance in estimating different parametric functions such as mean deviation about mean, standard deviation, standard error of mean and coefficient of variation with known mean. Numerical illustrations are given in support to the present study. Simulation studies confirm the high efficiency of the developed classes of shrinkage estimators when compared with their usual unbiased estimator and minimum mean squared error (MMSE) estimators. [ABSTRACT FROM AUTHOR]

A general procedure is considered for estimating the population mean of a study variate using double sampling for stratification based on auxiliary information. Expressions for bias and variance of the proposed classes of estimators up to the first degree of approximation are derived. An asymptotically optimum estimator in the classes is identified with their approximate variance formulae. The proposed classes of estimators are compared with the corresponding class of estimators based on unstratified double sampling. [ABSTRACT FROM AUTHOR]

In this paper we have suggested a double sampling version of Sahai [10] estimator using simple random sampling without replacement (SRSWOR) at both phases and its properties are studied. The estimator is also compared with simple mean per unit for a given cost of the survey. An empirical study is carried out to demonstrate the performance of suggested estimator over usual estimators. [ABSTRACT FROM AUTHOR]