Abstract #301061

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JSM 2003 Abstract #301061
Activity Number: 445
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #301061
Title: Modeling and Inference for Spatial Processes with Ordinal Data
Author(s): Vadim Kutsyy*+ and Vijayan N. Nair
Companies: University of Michigan and University of Michigan
Address: 553 Tyndall St., Los Altos, CA, 94022,
Keywords: latent variable ; spatial processes ; ordinal data ; random field ; semiconductor manufacturing ; pseudo-likelihood
Abstract:

This talk deals with a latent variable model for analyzing spatially dependent ordered categorical data. The ordinal data are assumed to be generated from an underlying Gaussian Markov random field. Both the homogeneous case as well as spatial regression problems can be handled. The goal is to estimate the underlying cell probabilities, the latent measure of dependence, and the regression parameters. Maximum likelihood estimation is computationally intractable, so we study alternative methods based on a pseudo-likelihood (PLE) and approximate MLEs. Large-sample properties of the PLE are derived, abd simulations results are used to compare the perfromances of the various methods. An application to semiconductor manufacturing is used to motivate the problem.

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