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  • Statistics In Transition

 

Article | 24-August-2017

ESTIMATING SENSITIVE POPULATION PROPORTION USING A COMBINATION OF BINOMIAL AND HYPERGEOMETRIC RANDOMIZED RESPONSES BY DIRECT AND INVERSE MECHANISM

individuals possessing those sensitive attributes can potentially eliminate the bias. Following Chaudhuri and Dihidar (2014) and Dihidar (2016), here, as a possible variant, we have made an attempt to estimate the sensitive population proportion using a combination of binomial and hypergeometric randomized responses by direct and inverse mechanism. Along with the traditional simple random sampling, with and without replacement, we consider here sampling of respondents by unequal probabilities. Essential

Kajal Dihidar, Manjima Bhattacharya

Statistics in Transition New Series, Volume 18 , ISSUE 2, 193–210

Research Article | 22-January-2018

A GENERALIZED RANDOMIZED RESPONSE MODEL

Housila P. Singh, Swarangi M. Gorey

Statistics in Transition New Series, Volume 18 , ISSUE 4, 669–686

Research Communicate | 18-March-2020

Generalized exponential estimators for the finite population mean

This study proposes a new class of exponential-type estimators in simple random sampling for the estimation of the population mean of the study variable using information of the population proportion possessing certain attributes. Theoretically, mean squared error (MSE) equations of the suggested ratio exponential estimators are obtained and compared with the Naik and Gupta (1996) ratio and product estimators, the ratio and product exponential estimator presented in Singh et al. (2007) and the

Tolga Zaman

Statistics in Transition New Series, Volume 21 , ISSUE 1, 159–168

Article | 22-January-2018

BAYESIAN ESTIMATION OF MEASLES VACCINATION COVERAGE UNDER RANKED SET SAMPLING

The present article is concerned with the problem of estimating an unknown population proportion p, say, of a certain population characteristic in a dichotomous population using the data collected through ranked set sampling (RSS) strategy. Here, it is assumed that the proportion p is not fixed but a random quantity. A Bayes estimator of p is proposed under squared error loss function assuming that the prior density of p belongs to the family of Beta distributions. The performance of the

Radhakanta Das, Vivek Verma, Dilip C. Nath

Statistics in Transition New Series, Volume 18 , ISSUE 4, 589–608

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