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Rajesh Singh

Publications and source records attributed to Rajesh Singh.

At least 73 records · Page 4Linked to original sources
arXiv2014

A New Estimator For Population Mean Using Two Auxiliary Variables in Stratified random Sampling

In this paper, we suggest an estimator using two auxiliary variables in stratified random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data. Key words: Study variable, auxiliary variable, stratified random sampling, bias and mean squared error.

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arXiv2014

Some Improved Estimators In Systematic Sampling Under Non-response

In this paper we have considered the problem of estimating the population mean in systematic sampling using information on an auxiliary variable in presence of non response. Some modified ratio, product and difference type estimators in systematic sampling have been suggested and their properties are studied. The expressions of mean squared errors (MSEs) up to the first order of approximation are derived. An empirical study is carried out to judge the best estimator out of the suggested estimators.

stat.ME↗
arXiv2014

Improved estimator of population variance using information on auxiliary attribute in simple random sampling

Singh and Kumar (2011) suggested estimators for calculating population variance using auxiliary attributes. This paper proposes a family of estimators based on an adaptation of the estimators presented by Kadilar and Cingi (2004) and Singh et al. (2007), and introduces a new family of estimators using auxiliary attributes. The expressions of the mean square errors (MSEs) of the adapted and proposed families are derived. It is shown that adapted estimators and suggested estimators are more efficient than Singh and Kumar (2011) estimators. The theoretical findings are supported by a numerical example.

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arXiv2014

Efficient class of estimators for population median using auxiliary information

This article suggests an efficient class of estimators of population median of the study variable using an auxiliary variable. Asymptotic expressions of bias and mean square error of the proposed class of estimators have been obtained. Asymptotic optimum estimator has been investigated along with its approximate mean square error. We have shown that proposed class of estimator is more efficient than estimator considered by Srivastava (1967), Gross (1980), Kuk and Mak (1989) Singh et al. (2003b), Al and Chingi (2009) and Singh and Solanki (2013). In addition theoretical findings are supported by an empirical study based on two populations to show the superiority of the constructed estimators over others.

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arXiv2014

Improved ratio type estimator using two auxiliary variables under second order approximation

In this paper, we have proposed a new Ratio Type Estimator using auxiliary information on two auxiliary variables based on Simple random sampling without replacement (SRSWOR). The proposed estimator is found to be more efficient than the estimators constructed by Olkin (1958), Singh (1965), Lu (2010) and Singh and Kumar (2012) in terms of second order mean square error.

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arXiv2013

Improved estimators in simple random sampling when study variable is an attribute

This article addresses the problem of estimating the population mean in the presence of auxiliary information when study variable itself is qualitative in nature. Bias and mean squared error (MSE) expressions of the class of estimators are derived up to the first order of approximation. The suggested estimators have been compared with the traditional estimator and several other estimators considered by Singh (2010). In addition, we support this theoretical result by an empirical study to show the superiority of the constructed estimator over others.

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arXiv2013

A family of estimators for estimating the population mean in simple random sampling under measurement errors

In this article we have suggested an improved estimator for estimating the population mean in simple random sampling using auxiliary information under the presence of measurement errors. The mean square error (MSE) of the proposed estimator has been derived under large sample approximation. Besides, considering the minimum case of the MSE equation, the efficient conditions between the proposed and existing estimators are obtained. These theoretical findings are supported by a numerical example.

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arXiv2013

Multivariate ratio estimation with known population proportion of two auxiliary characters for finite population

In the present study, we propose estimators based on geometric and harmonic mean for estimating population mean using information on two auxiliary attributes in simple random sampling. We have shown that, when we have multi-auxiliary attributes, estimators based on geometric mean and harmonic mean are less biased than Olkin (1958), Naik and Gupta (1996) and Singh (1967) type- estimator under certain conditions. However, the MSE of Olkin(1958) estimator and geometric and harmonic estimators are same up to the first order of approximation.

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arXiv2013

A generalised family of ratio product estimator using transformation equation

In this paper we deal with the estimation of population variance of the study variable y using auxiliary information on variable x. A family of ratio and product-type estimators are proposed using suitable transformation on both random variable x(auxiliary variable) and(study variable).Up to the first order of approximation the expression of mean square error and Bias term are obtained. An empirical study is carried out to illustrate the performance of the constructed estimator over others.

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arXiv2013

Some improved estimators for estimating population mean in stratified random sampling

Some improved estimators are proposed for estimating the population mean in stratified sampling in the presence of auxiliary information. Mean square error (MSE) of the proposed estimators have been derived under large sample approximation. It has been shown that under optimum conditions proposed estimators are better than usual unbiased estimator and Hansen (1946) estimator. Both theoretical and empirical findings are encouraging and support the soundness of the proposed procedure for mean estimation.

math.ST↗
arXiv2013

Use of Auxiliary Information in Variance Estimation

This paper proposes a class of ratio type estimators of finite population variance, when the population variance of an auxiliary character is known. Asymptotic expression for mean square error (MSE) is derived and compared with the mean square errors of some existing estimators. An empirical study is carried out to illustrate the performance of the constructed estimator over others.

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arXiv2013

Improved Family of Estimators of Population Mean in Simple Random Sampling

In this paper, a procedure is given for estimating the population mean in simple random sampling without replacement in the presence of auxiliary information. The mean squared error expressions of the proposed estimators have been derived under large sample approximation. We have compared the performances of the proposed estimators with several existing estimators. Both theoretical and empirical findings are encouraging and support the soundness of the proposed procedure for mean estimation.

math.ST↗
arXiv2013

On Improvement in Estimating Population Parameter(s) Using Auxiliary Information

The purpose of writing this book is to suggest some improved estimators using auxiliary information in sampling schemes like simple random sampling and systematic sampling. This volume is a collection of five papers. The following problems have been discussed in the book: In chapter one an estimator in systematic sampling using auxiliary information is studied in the presence of non-response. In second chapter some improved estimators are suggested using auxiliary information. In third chapter some improved ratio-type estimators are suggested and their properties are studied under second order of approximation. In chapter four and five some estimators are proposed for estimating unknown population parameter(s) and their properties are studied. This book will be helpful for the researchers and students who are working in the field of finite population estimation.

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arXiv2013

An Improved Estimator In Systematic Sampling

In this paper we have adapted Bahl and Tuteja (1991) estimator in systematic sampling using auxiliary information. Using Bedi (1996) transformation an improved estimator is also proposed under systematic sampling. The expressions of bias and mean square error up to the first order of approximation are derived. It has been shown that the proposed estimator performs better than many existing estimators. A numerical example is included to support the theoretical results.

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arXiv2013

A general class of regression type estimators in systematic sampling under non-response

In this paper we have proposed a general class of modified regression type estimator in systematic sampling under non-response to estimate the population mean using auxiliary information. The expressions of bias and mean square error (MSE) up to the first order approximations are derived. A numerical study is included to support the theoretical results.

stat.ME↗
arXiv2013

A general family of dual to ratio-cum-product estimator in sample surveys

This paper presents a family of dual to ratio-cum-product estimators for the finite population mean. Under simple random sampling without replacement (SRSWOR) scheme, expressions of the bias and mean-squared error (MSE) up to the first order of approximation are derived. We show that the proposed family is more efficient than usual unbiased estimator, ratio estimator, product estimator, Singh estimator (1967), Srivenkataramana (1980) and Bandyopadhyaya estimator (1980) and Singh et.al. (2005) estimator. An empirical study is carried out to illustrate the performance of the constructed estimator over others.

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arXiv2013

A General Family of Estimators for Estimating Population Mean in Systematic Sampling Using Auxiliary Information in the Presence of Missing Observations

This paper proposes a general family of estimators for estimating the population mean in systematic sampling in the presence of non-response adapting the family of estimators proposed by Khoshnevisan et al. (2007). In this paper we have discussed the general properties of the proposed family including optimum property. The results have been illustrated numerically by taking an empirical population considered in the literature.

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