Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72020
Sobita Sapam, Bikas K. Sinha, KK Singh Meitei
With reference to a Gauss-Markov Model, Analysis of Covariance (ANCOVA) is a standard exercise in the study of differential treatment effects in the presence of covariates. Again in the presence of ‘Neighbor Effects’, we carry out necessary data analysis in a routine manner. In this paper we present a review of this area of research, encompassing both covariates’ effects and neighbor effects. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 65-73
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Pub Date : 2024-03-28DOI: 10.3329/ijss.v24i1.72025
E. P. Clement, E. I. Enang
This study introduces the concept of inverse exponentiation in formulating calibration weights in stratified double sampling and proposes a more improved calibration estimator based on Koyuncu and Kadilar (2014) calibration estimator. The variance of the proposed logarithmic calibration estimator has been derived under large sample approximation. Calibration asymptotic optimum estimator and its approximate variance estimator are derived for the proposed logarithmic calibration estimator. Results of empirical study showed that the proposed logarithmic calibration estimator performs better than the Koyuncu and Kadilar (2014) calibration estimator with appreciable gains in efficiency. Also, simulation study for the comparison of the proposed logarithmic estimator with a Global estimator [Generalized Regression (GREG) estimator ] proved the robustness of the proposed logarithmic calibration estimator and by extension the efficacy of inverse exponentiation in calibration weightings. Analysis and evaluation are presented. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 91-102
{"title":"On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling","authors":"E. P. Clement, E. I. Enang","doi":"10.3329/ijss.v24i1.72025","DOIUrl":"https://doi.org/10.3329/ijss.v24i1.72025","url":null,"abstract":"This study introduces the concept of inverse exponentiation in formulating calibration weights in stratified double sampling and proposes a more improved calibration estimator based on Koyuncu and Kadilar (2014) calibration estimator. The variance of the proposed logarithmic calibration estimator has been derived under large sample approximation. Calibration asymptotic optimum estimator and its approximate variance estimator are derived for the proposed logarithmic calibration estimator. Results of empirical study showed that the proposed logarithmic calibration estimator performs better than the Koyuncu and Kadilar (2014) calibration estimator with appreciable gains in efficiency. Also, simulation study for the comparison of the proposed logarithmic estimator with a Global estimator [Generalized Regression (GREG) estimator ] proved the robustness of the proposed logarithmic calibration estimator and by extension the efficacy of inverse exponentiation in calibration weightings. Analysis and evaluation are presented.\u0000International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 91-102","PeriodicalId":512956,"journal":{"name":"International Journal of Statistical Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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