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Activity Number: 194
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #321046
Title: Kernel Smoothing for Hierarchical Data
Author(s): Christopher Wilson* and Patrick Gerard
Companies: and Clemson University
Keywords: Hierarchical data ; Bandwidth Selection ; Resampling ; Kernel Density esetimaion ; Dependent Data

Multistage sampling is a common sampling technique in many studies. A challenge presented by multistage sampling schemes is that an additional random term should be introduced to the linear model. Observations are identically distributed but not independent, thus many traditional kernel smoothing techniques, which assume that the data independent and identically distributed, may not produce reasonable estimates for the marginal density. Breunig (2001) proposed a method to account for the intra-class correlation leading to a complex bandwidth involving high order derivatives for bivariate kernel density estimate. We consider an alternative approach where the data are grouped into multiple random samples, by taking one observation each class, then constructing a kernel density estimate for each sample. A weighted average of these kernel density estimates yields a simple expression for the optimal bandwidth that accounts for the intra-class correlation. For unbalanced data, resampling methods are implemented to ensure that each class is included every random sample. Both simulation and analytical results are provided.

Authors who are presenting talks have a * after their name.

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