Dr. D.K. Yadav | National Institute of Health and Family Welfare (NIHFW) (original) (raw)

Papers by Dr. D.K. Yadav

The present article deals with the predictive estimation of finite population mean using coeffici... more The present article deals with the predictive estimation of finite population mean using coefficient of kurtosis and median of the auxiliary variable under simple random sampling scheme. Motivated by Milton et al. [2017], we have proposed an improved ratio type predictive estimator of finite population mean. Bias and mean squared error (MSE) of the proposed estimator are also obtained up to first order of approximation. Theoretical efficiency comparison of proposed estimator with Bahl and Tuteja [1991] estimator and Singh et al. estimator [2014] has also been carried out. Optimum conditions, under which the proposed estimator performs better than the competing estimators are also derived. To amply corroborate the theoretical findings, an empirical study has also been carried out. The percent relative efficiencies of the proposed estimator over existing estimators have also been obtained. The suitability of the proposed estimator can be established and appreciated as it has lesser mean squared error, when compared to other widely used estimators.

The present paper concerns with the estimation of population mean of the study variable by utiliz... more The present paper concerns with the estimation of population mean of the study variable by utilizing the known median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been made with the mean per unit estimator, usual ratioestimator of Cochran (1940), usual regression estimator of Watson (1937) ,Bahl and Tuteja estimator (1991), Kadilar (2016) and Subramani (2016) estimators. Through the numerical study, the theoretical findings are validated and it has been found that proposed estimatorperforms better than theexisting estimators.

In the present manuscript, we have proposed a new efficient class of estimators for the populatio... more In the present manuscript, we have proposed a new efficient class of estimators for the population variance of the study variable using information on the auxiliary variable. The expressions for bias and mean square error (MSE) of the proposed estimator are obtained up to the first order of approximation. An optimum estimator for the proposed estimator is also obtained and its optimum properties are studied. It is shown that the proposed estimator is more efficient than sample variance, traditional ratio estimator due to Isaki (1983), Singh et al. (2011) exponential ratio estimator, estimator based on Kadilar and Cingi (2003) ratio estimator for the population mean etc. estimators under optimum conditions. For illustration, an empirical study is also carried out.

In the present article, motivated by Jeelani et al. (2013), we have made an attempt to develop an... more In the present article, motivated by Jeelani et al. (2013), we have made an attempt to develop an improved ratio type estimator of population mean using predictive method of estimation by using linear combination of coefficient of skewness and the quartile deviation of auxiliary variable. The mathematical expressions for the bias and mean squared error (MSE) of the proposed estimator up to the first order approximation have been derived. Theoretical efficiency comparison of proposed estimator with the usual ratio estimator, usual product estimator, and Singh et al. (2014) estimators is also made. To amply corroborate the theoretical findings, an empirical study has also been carried out. The suitability of the proposed estimator can be established and appreciated as it has lesser mean squared error, when compared to other widely used estimators.

The present article addresses the effects of measurement errors on the estimation of population c... more The present article addresses the effects of measurement errors on the estimation of population coefficient of variation C Y of the study variable Y. Bias and mean squared error (MSE) of the proposed estimator are derived upto the first order of approximation under simple random sampling design. A theoretical efficiency comparison is made between the proposed estimator and the usual coefficient of variation estimator in presence of measurement errors. Based on large sample approximations the optimal condition is obtained under which the proposed estimator performs better than the conventional estimator in presence of measurement errors. Theoretical results are verified by the simulation study using R software.

The present manuscript pertains to an efficient ratio type predictive estimator for estimating fi... more The present manuscript pertains to an efficient ratio type predictive estimator for estimating finite population mean using the information on median of the study variable. Mathematical expressions for the bias and mean squared error (MSE) of the proposed predictive estimator have been obtained upto the first order of approximation. A theoretical efficiency comparison of the proposed estimator has been made with the Singh et al.(2014) and Yadav and Mishra (2015) estimators under predictive modelling approach. Theoretical findings are validated through the numerical study, and it has been found that proposed estimator performs better than the existing estimators.

The present paper advocates the estimation of population mean of the study variable by utilizing ... more The present paper advocates the estimation of population mean of the study variable by utilizing the information on median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been made with the mean per unit estimator, usual ratio of Cochran (1940) and usual regression estimator of Watson (1937),Bahl and Tuteja (1991)estimator, Kadilar (2016) and Subramani (2016) estimators. Theoretical results are supported by the numerical illustration and foundthat proposed estimatorperforms better than theexisting estimators.

This present paper concerns with the estimation of population mean of the study variable by utili... more This present paper concerns with the estimation of population mean of the study variable by utilizing the known median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been madewith the mean per unit estimator, usual ratio estimator of Cochran (1940), usual regression estimator of Watson (1937),Bahl and Tuteja (1991), Kadilar (2016) and Subramani (2016) estimators. Through the numerical study, the theoretical findings are validated and it has been found that proposed estimate or performs better than the existing estimators.

The present manuscript examines the effects of measurement errors on a regression type estimator ... more The present manuscript examines the effects of measurement errors on a regression type estimator used for estimation of finite population mean. The bias and mean square error (MSE) of the proposed estimator are obtained up to first order of approximation. Theoretical Efficiency comparison is also done between the proposed estimator and the usual linear regression estimator. The results have been illustrated by carrying out the simulation study using R software.

The present article concerns with the estimation of finite population mean using auxiliary inform... more The present article concerns with the estimation of finite population mean using auxiliary information in the presence of measurement errors. The expressions for the bias and mean squared error (MSE) of the proposed estimator are obtained up to first order of approximation. A theoretical efficiency comparison between the proposed estimator and usual linear regression estimator under measurement errors has been made. Theoretical results are supported by the simulation study using R software.

The present paper advocates the problem of estimating population variance when the measurement er... more The present paper advocates the problem of estimating population variance when the measurement errors are present in both the study variable and the auxiliary variable.Bias and Mean Square Error (MSE) of the proposed estimator is obtained up to first order of approximation. Theoretical efficiency comparison between usual variance estimator and the proposed estimator is also made under measurement errors.Theoretical results are supported by simulation study using R software.

The present paper deals with the effects of measurement errors on a regression-type estimator for... more The present paper deals with the effects of measurement errors on a regression-type estimator for estimating population mean using known coefficient of variation. The proposed estimator has made the use of auxiliary information to improve efficiency under the assumption that measurement error is present both in study and auxiliary variable. The bias and mean square error of proposed estimator are found. A comparative study with mean per unit estimator under measurement errors has also been made. Theoretical conclusions are verified by the empirical study.

In this article, an estimation procedure for the population variance utilizing auxiliary informat... more In this article, an estimation procedure for the population variance utilizing auxiliary information and known coefficient of variation is proposed. The Bias and mean square error of proposed estimator are found up to first order of approximation. A comparative study with the usual unbiased estimator and usual ratio estimator for population variance has been made. Numerical study is also given at the end of the article to support the theoretical findings.

In the present article, an improved estimator (s 2 y k) over usual unbiased estimator of populati... more In the present article, an improved estimator (s 2 y k) over usual unbiased estimator of population variance (s 2 y) is proposed by using known coefficient of variation (C y) of the study variable y. Asymptotic expression for its bias and mean square error (MSE) have been obtained. For more practical utility the study of proposed estimator under estimated optimum value of k has also been carried out. A comparative study has been made between the proposed estimator and the conventional estimator. Numerical illustration is also given in support of the present study.

In this paper the problem of estimating finite population variance under measurement errors is di... more In this paper the problem of estimating finite population variance under measurement errors is discussed. Some estimators based on arithmetic mean, geometric mean and harmonic mean under measurement errors are proposed. Biases and mean square errors of proposed estimators are calculated to the first order of approximation. A comparative study is made among the usual unbiased estimator, usual ratio estimator and Kadilar and Cingi(2006a) estimator. Hypothetical study is also given at the end of the paper to support the theoretical findings.

The present article considers the problem of finite population mean estimation, when the non-resp... more The present article considers the problem of finite population mean estimation, when the non-response and measurement errors are present simultaneously utilizing known information on coefficient of variation of study variable. We have developed an estimator of population mean which is improved and efficient, using Hansen and Hurwitz (1946) technique. Asymptotic expressions of the bias and variance of suggested estimator have been found correct up to approximation of degree one.The optimum value of characterizing scalar for which variance of proposed estimator is minimum has also been calculated. We have made a theoretical efficiency comparison of proposed estimator with usual Hansen Hurwitz estimator. To amply corroborate the theoretical findings, a simulation study has also been carried out using R software.

The present article addresses the problem of estimating the finite population mean under predicti... more The present article addresses the problem of estimating the finite population mean under predictive modeling approach. Ratio type predictive estimator of finite population mean using Tri-Mean and Quartile Average of the auxiliary variable is proposed for this purpose. The asymptotic expressions of bias and MSE are also obtained. Theoretical efficiency comparison of the proposed estimator with Bahl and Tuteja estimator (1991) and Singh et al. estimator (2014) has also been made. Theoretical results are also supported by numerical illustration.

In this scripture, we ponder the problem of efficient estimation of population mean of study vari... more In this scripture, we ponder the problem of efficient estimation of population mean of study variable utilizing information on highly correlated auxiliary variables under the presence of non-response on either of the variables. For this purpose, we suggest, an improved estimator under three different situations of non-response. Under the first situation, estimation of population mean is done with the problem of non-response on both the study and the auxiliary variables with the additional condition that the population means of the auxiliary variables are known. The second situation is to estimate the population mean of primary variable when the problem of non-response is only on the primary variable but the population means of the auxiliary variables are known while under the third situation estimation is performed with the problem of non-response on both the study and the auxiliary variables but population mean of one of the auxiliary variables is unknown. We study the sampling properties of the suggested estimator under above three different situations of non-response. We compare the proposed estimator with the competing estimators of population mean, under three different situations of non-response. The efficiency conditions are obtained for all three situations. A numerical study is also carried out to verify the efficiency conditions.