Double sampling designs in multivariate linear models with missing variables

Noam Cohen; Ori Davidov; Yoel Haitovsky

doi:10.1080/03610910801885993

Double sampling designs in multivariate linear models with missing variables

Noam Cohen, Ori Davidov, Yoel Haitovsky

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

We provide a method for finding the optimal double sampling plan for estimating the mean value of a continuous outcome. It is assumed that the fallible and true outcome data are related by a multivariate linear regression model where only some of the explanatory variables are sampled. Conditions under which double sampling is preferred over standard sampling plans are determined. An application of the method to a well-known data set on air pollution is presented.

Original language	English
Pages (from-to)	1156-1166
Number of pages	11
Journal	Communications in Statistics Part B: Simulation and Computation
Volume	37
Issue number	6
DOIs	https://doi.org/10.1080/03610910801885993
State	Published - Jun 2008

Keywords

Double sampling
Missing variables
Optimal design

ASJC Scopus subject areas

Statistics and Probability
Modeling and Simulation

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10.1080/03610910801885993

Cite this

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Double sampling designs in multivariate linear models with missing variables

Abstract

Keywords

ASJC Scopus subject areas

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