JSM 2005 - Toronto

Abstract #303569

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 74
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 8:00 PM to 9:50 PM
Sponsor: Biometrics Section
Abstract - #303569
Title: Using Conditional Residuals To Assess Goodness of Fit in Hidden Markov Models
Author(s): Theodore Lystig*+
Companies: AstraZeneca
Address: Pepparedsleden 1, Molndal, 431 50, Sweden
Keywords: generalized estimating equations ; goodness of fit ; hidden Markov models ; conditional residuals ; longitudinal data
Abstract:

Hidden Markov models (HMMs) provide an elegant framework for specifying long-range dependencies in longitudinal data. They can be thought of as prototypical examples of the increasingly popular class of models known as latent variable models. I present graphical and numerical techniques for evaluating goodness of fit (GOF) in HMMs. The GOF techniques presented are used to assess potentially omitted covariates that act on either the transition matrix of hidden states or the output process for the observed states. Conditional residuals, constructed using the probability of a given output state given the prior history, are shown to have strong power for detecting model misspecification.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005