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Dive into the research topics where Rajesh Tailor is active.

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Featured researches published by Rajesh Tailor.


Communications for Statistical Applications and Methods | 2011

A Generalized Ratio-cum-Product Estimator of Finite Population Mean in Stratified Random Sampling

Rajesh Tailor; Balkishan Sharma; Jong-Min Kim

This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.


Journal of Statistics and Management Systems | 2017

Estimation of population variance in simple random sampling

Hilal A. Lone; Rajesh Tailor

Abstract In this paper, an estimator in simple random sampling has been proposed to estimate the population variance. Conditions under which the proposed estimator is more efficient than usual unbiased estimator and estimators given by Isaki (1983), Kadilar and Cingi (2006), Upadhyaya and Singh (1999), Singh and Solanki (2013), Singh et al. (1973), Das and Tripathi (1978), Singh et al.(1988), Prasad and Singh (1992), Garcia and Cebrian (1996) , Upadhyaya and Singh (2001) and Shabbir and Gupta (2006) are obtained. The bias and mean squared error of the proposed estimator is obtained up to the first degree of approximation. To judge the merits of the proposed estimator over other estimators, we consider three numerical examples.


Communications in Statistics-theory and Methods | 2014

Hartley-Ross Type Estimators for Population Mean Using Known Parameters of Auxiliary Variate

Housila P. Singh; Balkishan Sharma; Rajesh Tailor

This article proposes Hartley-Ross type unbiased estimators of finite population mean using information on known parameters of auxiliary variate when the study variate and auxiliary variate are positively correlated. The variances of the proposed unbiased estimators are obtained. It has been shown that the proposed estimators are more efficient than the simple mean estimator, usual ratio estimator and estimators proposed by Sisodia and Dwivedi (1981), Kadilar and Cingi (2006), and Kadilar et al. (2007) under certain realistic conditions. Empirical studies are also carried out to demonstrate the merits of the proposed unbiased estimators over other estimators considered in this article.


Korean Journal of Applied Statistics | 2012

Families of Estimators of Finite Population Variance using a Random Non-Response in Survey Sampling

Housila P. Singh; Rajesh Tailor; Jong-Min Kim; Sarjinder Singh

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.


Journal of Statistical Computation and Simulation | 2014

General procedure for estimating the population mean using ranked set sampling

Housila P. Singh; Rajesh Tailor; Sarjinder Singh

In this paper, we suggest a class of estimators for estimating the population mean Ȳ of the study variable Y using information on X̄, the population mean of the auxiliary variable X using ranked set sampling envisaged by McIntyre [A method of unbiased selective sampling using ranked sets, Aust. J. Agric. Res. 3 (1952), pp. 385–390] and developed by Takahasi and Wakimoto [On unbiased estimates of the population mean based on the sample stratified by means of ordering, Ann. Inst. Statist. Math. 20 (1968), pp. 1–31]. The estimator reported by Kadilar et al. [Ratio estimator for the population mean using ranked set sampling, Statist. Papers 50 (2009), pp. 301–309] is identified as a member of the proposed class of estimators. The bias and the mean-squared error (MSE) of the proposed class of estimators are obtained. An asymptotically optimum estimator in the class is identified with its MSE formulae. To judge the merits of the suggested class of estimators over others, an empirical study is carried out.


Data Science Journal | 2009

A Modified Ratio-Cum-Product Estimator of Finite Population Mean in Stratified Random Sampling

Rajesh Tailor

This paper suggests a ratio-cum-product estimator of finite population mean using a correlation coefficient between study variate and auxiliary variate in stratified random sampling. Bias and mean squared expressions of the suggested estimator are derived and compared with combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). An empirical study is also carried out to examine the performance of the proposed estimator.


Communications in Statistics-theory and Methods | 2009

Estimation of Ratio of Two Finite-Population Means in the Presence of Non Response

Sarjinder Singh; Housila P. Singh; Rajesh Tailor; Jack Allen; Marcin Kozak

We propose a wide class of estimators of the ratio of two finite-population means in the presence of random non response. Four estimators proposed by Toutenburg and Srivastava (1998) are shown as special cases of the proposed class of estimators. Linear models are developed to estimate the relative efficiency of the proposed class of estimators with respect to the above four estimators.


Communications in Statistics-theory and Methods | 2014

Ratio-Cum-Product Type Exponential Estimator of Finite Population Mean in Stratified Random Sampling

Rajesh Tailor; Sunil Chouhan

This article addresses the problem of estimating the finite population mean in stratified random sampling using auxiliary information. Motivated by Singh (1967) and Bahl and Tuteja (1991) a ratio-cum-product type exponential estimator has been suggested and its bias and mean squared error have been derived under large sample approximation. Suggested estimator has been compared with usual unbiased estimator of population mean in stratified random sampling, combined ratio estimator, combined product estimator, ratio and product type exponential estimator of Singh et al. (2008). Conditions under which suggested estimator is more efficient than other considered estimators have been obtained. A numerical illustration is given in support of the theoretical findings.


Communications in Statistics-theory and Methods | 2016

Generalized ratio-cum-product type exponential estimator in stratified random sampling

Hilal A. Lone; Rajesh Tailor; Housila P. Singh

ABSTRACT In this article, we propose a generalized ratio-cum-product type exponential estimator for estimating population mean in stratified random sampling. Asymptotic expression of the bias and mean squared error of the proposed estimator are obtained. Asymptotic optimum estimator in the proposed estimator has been obtained with its mean squared error formula. Conditions under which the proposed estimator is more efficient than usual unbiased estimator, combined ratio and product type estimators, Singh et al. (2008) estimators and Tailor and Chouhan (2014) estimator are obtained. An empirical study has also been carried out.


Data Science Journal | 2010

An Alternative Ratio-cum-Product Estimator of Population Mean Using a Coefficient of Kurtosis for Two Auxiliary Variates

Rajesh Tailor; Med Ram Verma; Balkishan Sharma

An alternative ratio-cum-product estimator of population mean using the coefficient of kurtosis for two auxiliary variates has been proposed. The proposed estimator has been compared with a simple mean estimator, the usual ratio estimator, a product estimator, and estimators proposed by Singh (1967) and Singh et al. (2004). An empirical study is also carried out in support of the theoretical findings.

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Med Ram Verma

Indian Veterinary Research Institute

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Neha Garg

Indira Gandhi National Open University

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Jong-Min Kim

University of Minnesota

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Rajiv Pandey

Hemwati Nandan Bahuguna Garhwal University

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