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

Abstract Details

Activity Number: 422
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract - #307166
Title: Nonparametric State Space Models for Longitudinal Data
Author(s): Peter Song and Daimin Shi*+
Companies: University of Michigan and Southwestern University of Finance and Economics
Address: School of Statistics, Chengdu, International, 610074, China
Keywords: Estimating equation ; Kalman smoother ; longitudinal data ; state space model
Abstract:

We will present a class of nonparametric state space models for the analysis of non-normal longitudinal data, in which the latent process follows a stationary first-order Markov model with exponential dispersion model margins. We propose to use the local linear fitting approach to estimating nonparametric functions through Jorgensen and Song's (2007) Kalman estimating equation. We illustrate the proposed approach via both simulated and real world data examples.


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