This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 685
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #306925
Title: Nonparametric Estimation of Response Probabilities in Survey Sampling: An Empirical Investigation
Author(s): Audrey Beliveau*+ and David Haziza
Companies: Université de Montréal and Université de Montréal
Address: 2920, chemin de la Tour, bureau 5190, Montreal, QC, H3T 1J4, Canada
Keywords: unit nonresponse ; nonparametric estimation ; response probability ; weighting classes

Nonresponse bias occurs when respondents and nonrespondents differ with respect to characteristics of interest. A common way of reducing the nonresponse bias is to adjust the design weight of the respondents. A popular weight adjustment procedure consists of dividing the design weight of respondents by their estimated response probability, which can be estimated using parametric methods (e.g., logistic regression model) or nonparametric methods. Nonparametric methods include kernel smoothing methods and local polynomial regression (Da Silva & Opsomer, 2006, 2009) or weight adjustment procedures applied within weighting classes (which is commonly used in practice). In this talk, results of a Monte Carlo simulation study comparing different nonparametric methods in terms of bias and mean square error will be presented.

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