Advanced class for variance estimation utilising known quartiles of the auxiliary variable

2021 ◽  
Vol 13 (3) ◽  
pp. 226
Author(s):  
Hari Sharma ◽  
Dinesh K. Sharma ◽  
S.K. Yadav
Keyword(s):  
Variance Estimation ◽  
Auxiliary Variable

scholarly journals Variance Estimation Using Median of the Auxiliary Variable

2012 ◽  
Vol 1 (3) ◽  
pp. 36-40 ◽  
Author(s):  
J. Subramani ◽  
G. Kumarapandiyan
Keyword(s):  
Variance Estimation ◽  
Auxiliary Variable

Unequal probability sampling

2019 ◽  
pp. 140-172
Author(s):  
David G. Hankin ◽  
Michael S. Mohr ◽  
Ken B. Newman
Keyword(s):  
Variance Estimation ◽  
Random Variable ◽  
Auxiliary Variable ◽  
Second Order ◽  
Systematic Sampling ◽  
Sampling Variance ◽  
Probability Sampling ◽  
Unequal Probability ◽  
Inclusion Probabilities ◽  
Selection Probabilities

Equal probability selection is a special case of the general theory of probability sampling in which population units may be selected with unequal probabilities. Unequal selection probabilities are often based on auxiliary variable values which are measures of the sizes of population units, thus leading to the acronym (PPS)—“Probability Proportional to Size”. The Horvitz–Thompson (1953) theorem provides a unifying framework for design-based sampling theory. A sampling design specifies the sample space (set of all possible samples) and associated first and second order inclusion probabilities (probabilities that unit i, or units i and j, respectively, are included in a sample of size n selected from N according to some selection method). A valid probability sampling scheme must have all first order inclusion probabilities > 00 (i.e., every population unit must have a chance of being in the sample). Unbiased variance estimation is possible only for those schemes that guarantee that all second order inclusion probabilities exceed zero, thus providing theoretical justification for the absence of unbiased estimators of sampling variance in systematic sampling and other schemes for which some second order inclusion probabilities are zero. Numerous generalized Horvitz–Thompson (HT) estimators can be formed and all are consistent estimators because they are functions of consistent HT estimators. Unequal probability systematic sampling and Poisson sampling (the unequal probability counterpart to Bernoulli sampling for which sample size is a random variable) are also considered. Several R programs for selecting unequal probability samples and for calculating first and second order inclusion probabilities are posted at http://global.oup.com/uk/companion/hankin.

scholarly journals Using Auxiliary Information More Efficiently in Population Variance Estimation - A New Family of Estimators

2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Kalim Ullah ◽  
Zawar Hussain ◽  
Salman Arif Cheema
Keyword(s):  
Finite Population ◽  
Variance Estimation ◽  
Theoretical Study ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
First Order ◽  
New Family ◽  
Squared Error ◽  
Estimation Of Variance

In this article, we have suggested estimation of variance in finite population by using known values of parameter related to auxiliary information such as rank and second raw moment of auxiliary variable in stratified random sampling. The expression for the bias and mean squared error (MSE) of the suggested estimator are obtained up to first order of approximation. The proposed estimator is efficient comparatively various other estimators. A numerical and theoretical study are performed to support the suggested estimator.

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