Estimation of population variance using regression type estimator under successive sampling

EasyChair Preprint 2907, version 1

Abstract

In the realm of successive sampling, most of the literature concerns with the estimation is population mean and no emphasis is laid on estimation of population variance. Motivated by Isaki's (1983) work of variance estimation, Singh et al. (2011) put their first effort on estimation of population variance under successive sampling. Thus, by cognizing aforementioned problem, we proposed a combined estimator for estimating population variance precisely and an analytical scenario is also presented for judging its properties. A numerical illustration, which validate the usefulness of the proposed estimator, based on hypothetical population is also mentioned.

Keyphrases: Mean Squared Error, Optimum replacement policy, Regression type estimator, Successive sampling, bias

@booklet{EasyChair:2907,
  author    = {Shashi Bhushan and Shailja Pandey},
  title     = {Estimation of population variance using regression type estimator under successive sampling},
  howpublished = {EasyChair Preprint 2907},
  year      = {EasyChair, 2020}}