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  • Statistics In Transition

 

Article | 13-December-2019

A NEW RATIO ESTIMATOR: AN ALTERNATIVE TO REGRESSION ESTIMATOR IN SURVEY SAMPLING USING AUXILIARY INFORMATION

The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and

Mir Subzar, S. Maqbool, T. A. Raja, Prayas Sharma

Statistics in Transition New Series, Volume 20 , ISSUE 4, 181–189

Research Communicate | 20-November-2017

SAMPLE ALLOCATION IN ESTIMATION OF PROPORTION IN A FINITE POPULATION DIVIDED AMONG TWO STRATA

Dominik Sieradzki, Wojciech Zieliński

Statistics in Transition New Series, Volume 18 , ISSUE 3, 541–548

Research Article | 18-March-2020

Parametric prediction of finite population total under Informative sampling and nonignorable nonresponse

In this paper, we combine two methodologies used in the model-based survey sampling, namely the prediction of the finite population total, named T, under informative sampling and full response, see Sverchkov and Pfeffermann  (2004), and the prediction of T with a noninformative sampling design and the nonignorable nonresponse mechanism, see Eideh (2012). The former approach involves the dependence of the first order inclusion probabilities on the study variable, while the latter involves

Abdulhakeem Eideh

Statistics in Transition New Series, Volume 21 , ISSUE 1, 13–35

Research Article | 01-June-2020

On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction

Adam Chwila, Tomasz Żądło

Statistics in Transition New Series, Volume 21 , ISSUE 2, 35–60

Article | 20-December-2020

Some linear regression type ratio exponential estimators for estimating the population mean based on quartile deviation and deciles

This paper deals some linear regression type ratio exponential estimators for estimating the population mean using the known values of quartile deviation and deciles of an auxiliary variable in survey sampling. The expressions of the bias and the mean square error of the suggested estimators have been derived. It was compared with the usual mean, usual ratio (Cochran (1977)), Kadilar and Cingi (2004, 2006) and Subzar et al. (2017) estimators. After comparison, the condition which makes the

Shakti Prasad

Statistics in Transition New Series, Volume 21 , ISSUE 5, 85–98

Article | 24-August-2017

SEQUENTIAL DATA WEIGHTING PROCEDURES FOR COMBINED RATIO ESTIMATORS IN COMPLEX SAMPLE SURVEYS

important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance

Aylin Alkaya, H. Öztaş Ayhan, Alptekin Esin

Statistics in Transition New Series, Volume 18 , ISSUE 2, 247–270

Research Article | 13-June-2021

Credibility of disability estimates from the 2011 population census in Poland

disabilities was observed. However, it should be emphasised that significant modifications were introduced to the methodology of the last census. Population census 2011 was the first census in Poland combing administrative data sources and the survey sampling method. The main objective of the study is to assess the quality of estimates relating to the number of disabled persons, obtained on the basis of the 2011 census data. It is a comparative study aimed at identifying the similarities and discrepancies

Elżbieta Gołata, Grażyna Dehnel

Statistics in Transition New Series, Volume 22 , ISSUE 2, 41–65

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