An Improved Exponential Method of Estimation for Current Population Mean in Two-Occasion Successive Sampling

Singh, G. N.; Singh, Chandraketu; Pandey, Awadhesh K.; Suman, S.

doi:10.18576/jsap/060314

Cited by 4 publications

(2 citation statements)

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“…Thus, the exponential estimator given by Bahl and Tuteja 2 may be treated as an alternative of regression estimators. Thereafter, following the lines of Bahl and Tuteja, 2 various notable authors such as Singh and Homa, 19 Priyanka and Mittal, 13 Singh et al, 20 Singh and Pal, 21 Singh et al 22 among others have proposed efficient estimators using an exponential function with effective results which are published in reputed journals. Since, exponential-type estimators are the better alternative for increasing the precision of the estimates.…”

Section: Successive Sampling Schemementioning

confidence: 99%

Study to Alter the Nuisance Effect of Non-Response Using Scrambled Mechanism

Singh¹,

Kamal²,

Singh³

et al. 2021

RMHP

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Introduction In biometric sample surveys, our objective is to get ready-made information for future planning and policy implementations related to the subject matters of highly sensitive issues. In such situations, we apply randomized response/scrambled response techniques. There are many highly sensitive issues which need to be examined over time as they may have a tendency to change. To get rid of these types of practical cases we need a scrambled response technique on successive occasions. Methods Using an additive and multiplicative technique, we proposed new effective scrambled response models to estimate the population mean of quantitative sensitive characteristics. Degree of privacy protection and unified measure approaches are used to examine the efficacy of the proposed models. Efficiency of the proposed models has been checked using MATLAB software. The utility of the proposed models under two occasions of successive sampling has been also explored using exponential-type estimators. Empirical and simulation studies are carried out to justify the proposition of the proposed estimators using MATLAB software. Results The percent relative efficiencies of the proposed models are always greater than 100 with respect to the well-known Bar-Lev et al model. In terms of degree of privacy protection, most of the values are greater than 0.5 and closer to 1. Similarly, the values of the proposed models are smaller with respect to the Bar-Lev et al model in terms of a unified measure approach. When the proposed scrambled response models are used on successive occasions, the percent relative efficiency is always found greater than 100 for all cases over its competitors. Discussion In this study, after deeply examining the properties of the proposed models, we found that the proposed models performed better over the well-known existing model. The proposed models may be used in human survey when we deal with highly sensitive issues. The proposed models also performed better when we utilized them in successive sampling. Hence, if sensitive characteristics change with time, the proposed estimators may be the best alternative to deal with these types of situations. Mathematics Subject Classification 62D05.

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Section: Successive Sampling Schemementioning

confidence: 99%

Study to Alter the Nuisance Effect of Non-Response Using Scrambled Mechanism

Singh¹,

Kamal²,

Singh³

et al. 2021

RMHP

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“…The theory of Patterson (1950) has been extended by Eckler (1955), Rao and Graham (1964), Cochran (1977), Sen 2 ISSN 2424-6271 IASSL (1971,1973), Sukhatme et al (1984) and Singh et al (1992), Singh et al (2014) among others. In many situations of practical importance, information on an auxiliary variable may be readily available on the first as well as on the second occasion, for instance, see Singh andVishwakarma (2007a, b, 2009), Singh and Kumar (2010), Kumar (2012) and Singh et al (2013) .The aim of this paper is to develop a procedure of utilizing the information on two auxiliary variables readily available on both the occasions. We have assumed a situation, where one auxiliary variable is positively correlated and the other auxiliary variable is negatively correlated with the study variable on the first and second occasions.…”

Section: Introductionmentioning

confidence: 99%

Improved Estimation of Current Population Mean Over Two Occasions

Singh

Pal

2015

Sri Lankan J App Stats

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This paper addresses the role of two auxiliary variables on both the occasions to improve the precision of estimates at current (second) occasion in two occasion successive sampling .Utilizing the readily available information on two auxiliary variables on both the occasions and information on the study variable from the previous occasion, an efficient estimation procedure of population mean on current (second) occasion has been envisaged. It is to be mentioned that out of the two auxiliary variables one auxiliary variable is positively correlated with the study variable while other is negatively correlated. Behavior of the proposed estimator has been studied and compared with the sample mean estimator, when there is no matching from the previous occasion and traditional successive sampling estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current (second) occasion. Optimal replacement policy is also discussed. In addition, we support the theoretical results with the aid of numerical examples.

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