Weighting adjustment is a popular method for handling nonresponse in sample surveys. It consists of multiplying the sampling weights of the respondents by the inverse of their estimated response probabilities. Typically, response probabilities are estimated by fitting parametric models relating response occurrence and auxiliary variables. An alternative solution is the nonparametric estimation of them. The aim of this paper is to investigate, via simulation experiments, the small sample properties of kernel regression estimation of the response probabilities when auxiliary information consists in various mixed data type variables.

Kerlel-Type Smoothing Methodsin the Nonresponse Context / E. Rocco. - STAMPA. - Atti del 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society - Book of short paper:(2009), pp. 605-608. ((Intervento presentato al convegno 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society tenutosi a Catania nel 9-11 Settembre 2009.

Kerlel-Type Smoothing Methodsin the Nonresponse Context

ROCCO, EMILIA
2009

Abstract

Weighting adjustment is a popular method for handling nonresponse in sample surveys. It consists of multiplying the sampling weights of the respondents by the inverse of their estimated response probabilities. Typically, response probabilities are estimated by fitting parametric models relating response occurrence and auxiliary variables. An alternative solution is the nonparametric estimation of them. The aim of this paper is to investigate, via simulation experiments, the small sample properties of kernel regression estimation of the response probabilities when auxiliary information consists in various mixed data type variables.
Classification and Data Analysis 2009 - 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society- Book of Short Papers
7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society
Catania
9-11 Settembre 2009
E. Rocco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/371602
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