Research paper | 31-October-2017
This paper addressed the problem of estimation of finite population mean in the case of post-stratification. Improved separate ratio and product exponential type estimators in the case of post-stratification are suggested. The biases and mean squared errors of the suggested estimators are obtained up to the first degree of approximation. Theoretical and empirical studies have been done to demonstrate better efficiencies of the suggested estimators than other considered estimators.
Hilal A. Lone,
Rajesh Tailor
Statistics in Transition New Series, Volume 16 , ISSUE 1, 53–64
Sampling Methods | 26-May-2018
Jaishree Prabha Karna,
Dilip Chandra Nath
Statistics in Transition New Series, Volume 19 , ISSUE 1, 25–44
Sampling Methods | 22-July-2018
In this article, we propose a class of generalized exponential type estimators to estimate the finite population mean by using two auxiliary variables under non-response in simple random sampling. The proposed estimator under non-response in different situations has been studied and gives minimum mean square error as compared to all other considered estimators. Usual exponential ratio type estimator, exponential product type estimator and many more estimators are also identified from the
Siraj Muneer,
Javid Shabbir,
Alamgir Khalil
Statistics in Transition New Series, Volume 19 , ISSUE 2, 259–276
Research Communicate | 18-March-2020
This study proposes a new class of exponential-type estimators in simple random sampling for the estimation of the population mean of the study variable using information of the population proportion possessing certain attributes. Theoretically, mean squared error (MSE) equations of the suggested ratio exponential estimators are obtained and compared with the Naik and Gupta (1996) ratio and product estimators, the ratio and product exponential estimator presented in Singh et al. (2007) and the
Tolga Zaman
Statistics in Transition New Series, Volume 21 , ISSUE 1, 159–168
Article | 22-January-2018
Kumari Priyanka,
Richa Mittal
Statistics in Transition New Series, Volume 18 , ISSUE 4, 569–587