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Asymptotic properties of a generalized regression‐type predictor of a finite population variance in probability sampling
Author(s) -
Shah D. N.,
Patel P. A.
Publication year - 1996
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315746
Subject(s) - mathematics , statistics , estimator , mean squared error , variance (accounting) , regression analysis , population , minimum variance unbiased estimator , sampling design , business , demography , sociology , accounting
A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design‐unbiased (ADU) and asymptotically design‐consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression‐type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design‐unbiased estimator when the superpopulation model is correct. The generalized regression‐type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.