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Activity Number: 120
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #315861 View Presentation
Title: Bayesian Predictive Inference for Skewed Survey Data in Unequal Probability Sampling
Author(s): Qixuan Chen*
Companies: Columbia University
Keywords: Bayesian modeling ; Penalized spline regression ; Population quantities ; Stan
Abstract:

Skewed data are common in sample surveys. We consider two types of non-normally distributed data, including skewed data and zero-contaminated skewed data. For the skewed data, we propose a skew-normal penalized spline model by assuming a skew-normal distribution given the sample selection probability and modeling the location and scale parameters of the skew-normal distribution as a penalized spline function of the selection probability. To model the zero-contaminated skew-normal data, we consider a two-stage approach by first modeling the probability of positive values using a probit penalized spline regression model (Chen, Elliott, and Little 2010) and then using a skew-normal penalized spline model for positive values. We assume that the probability of selection is known for all units in the population. Using a fully Bayesian approach, we can obtain the posterior predictive distributions of the non-sample units in the population, and thus the posterior distributions of the population quantities, such as means and quantiles. We compare our proposed estimator with alternative methods using simulations based on artificial data as well as a farm survey data.


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