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591 results on '"Singh, Housila P."'

333. An Efficient Alternative Mixed Randomized Response Procedure.

Author

Singh, Housila P. and Tarray, Tanveer A.

Subjects
*RANDOMIZED response, *RANDOMIZED response technique, *NUMERICAL analysis, *SOCIOLOGICAL research, *POPULATION research
Abstract

In this article, we have suggested a new modified mixed randomized response (RR) model and studied its properties. It is shown that the proposed mixed RR model is always more efficient than the Kim and Warde’s mixed RR model. The proposed mixed RR model has also been extended to stratified sampling. Numerical illustrations and graphical representations are also given in support of this study. [ABSTRACT FROM AUTHOR]

Published
2015
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334. A GENERAL PROCEDURE FOR ESTIMATING THE MEAN OF A SENSITIVE VARIABLE USING AUXILIARY INFORMATION.

Author

Tarray, Tanveer A. and Singh, Housila P.

Subjects
*ESTIMATION theory, *RANDOMIZED response, *RANDOMIZED response technique, *MEAN square algorithms, *ESTIMATION bias
Abstract

This paper proposes classes of estimators for the mean of sensitive variable utilizing information on a non - sensitive auxiliary variable. Expressions for the biases and mean square errors of the suggested estimators correct up to first order of approximation are derived. It has been shown that the suggested new class of estimators based on the known population mean X and variance Sx 2 of the auxiliary variable X are better than conventional unbiased estimators which do not utilize the auxiliary information, Sousa et al.'s (2010) ratio estimator and Gupta et al.'s (2012) regression estimator under a very realistic condition. [ABSTRACT FROM AUTHOR]

Published
2015

335. A STRATIFIED RANDOMIZED RESPONSE MODEL FOR SENSITIVE CHARACTERISTICS USING NEGATIVE BINOMIAL DISTRIBUTION.

Author

Tarray, Tanveer A. and Singh, Housila P.

Subjects
*RANDOMIZED response, *RANDOMIZED response technique, *NEGATIVE binomial distribution, *RESPONDENTS, *ESTIMATION theory
Abstract

Taking the clue from the pioneer work of Hussain et al.'s (2014) we have suggested a new stratified randomized response model. The properties of the suggested stratified randomized response model have been studied under proportional and "Neyman" allocations. The study has been also carried out using modified Neyman allocation in the presence of crude prior estimates. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

Published
2015

336. Some Classes of Estimators for Median Estimation in Survey Sampling.

Author

Solanki, Ramkrishna S. and Singh, Housila P.

Subjects
*MATHEMATICAL variables, *ESTIMATION theory, *SAMPLING (Process), *MEDIAN (Mathematics), *SURVEYS, *MEAN square algorithms
Abstract

In this article, we have suggested some classes of estimators for estimating finite population median using information on an auxiliary variable. To study the properties of suggested classes of estimators under large sample approximation, a generalized class of estimators has been suggested with its properties. It has been shown that the suggested classes of estimators are more efficient than other existing estimators. The results have been illustrated through an empirical study. [ABSTRACT FROM PUBLISHER]

Published
2015
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337. An Improved Procedure of Estimating the Finite Population Mean Using Auxiliary Information in the Presence of Random Non Response.

Author

Tailor, Rajesh, Sharma, Balkishan, and Singh, Housila P.

Subjects
ESTIMATION theory, APPROXIMATION theory, COMPARATIVE studies, PERFORMANCE evaluation, FINITE element method
Abstract

This article deals with the problem of estimation of the finite population mean using auxiliary information in the presence of random non response. Three different situations where random non response occurs either in study variate, or in auxiliary variate, or in both the variates, have been discussed. The asymptotically optimum estimators (AOEs) for each strategy are also identified. Expressions of biases and mean squared errors of the proposed estimators have been derived up to the first degree of approximation. Proposed estimators have been compared with the usual unbiased estimator, ratio estimator, and product estimator in the presence of random non response. Empirical studies are also carried out to show the performance of the proposed estimators over other estimators. [ABSTRACT FROM AUTHOR]

Published
2015
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