Towards optimal regression estimation in sample surveys

Summary The Montanari (1987) regression estimator is optimal when the population regression coefficients are known. When the coefficients are estimated, the Montanari estimator is not optimal and can be extremely volatile. Using design‐based arguments, this paper proposes a simpler and better altern...

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Bibliographic Details
Published inAustralian & New Zealand journal of statistics Vol. 45; no. 3; pp. 319 - 329
Main Authors Berger, Yves G., Tirari, Mohammed E.H., Tillé, Yves
Format Journal Article
LanguageEnglish
Published Oxford, UK and Boston, USA Blackwell Publishing Ltd 01.09.2003
Subjects
conditional Poisson sampling
design-based inference
Estimation
Evaluation
generalized regression estimator
inclusion probabilities
Model testing
Montanari estimator
Population
Samples
Statistical analysis
Statistical methods
Surveys
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Summary:Summary The Montanari (1987) regression estimator is optimal when the population regression coefficients are known. When the coefficients are estimated, the Montanari estimator is not optimal and can be extremely volatile. Using design‐based arguments, this paper proposes a simpler and better alternative to the Montanari estimator that is also optimal when the population regression coefficients are known. Moreover, it can be easily implemented as it involves standard weighted least squares. The estimator is applicable under single stage stratified sampling with unequal probabilities within each stratum.
Bibliography:istex:9A16466F0DB7E685EDCBEDC3B4C482C139133313
ark:/67375/WNG-5WDQ8C5W-Q
ArticleID:ANZS286
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1369-1473
1467-842X
DOI:10.1111/1467-842X.00286