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Some Variants of Constrained Estimation in Finite Population Sampling
Author(s) -
Ghosh Malay,
Steorts Rebecca C.
Publication year - 2019
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12309
Subject(s) - computer science , benchmarking , estimation , imputation (statistics) , divergence (linguistics) , simple (philosophy) , population , calibration , mathematical optimization , sampling (signal processing) , mathematics , statistics , machine learning , missing data , engineering , linguistics , philosophy , demography , systems engineering , epistemology , filter (signal processing) , marketing , sociology , business , computer vision
Summary This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.