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Assessment of Model Adequacy for Markov Regression Time Series Models

Larry F. Léon and Chih-Ling Tsai
Biometrics
Vol. 54, No. 3 (Sep., 1998), pp. 1165-1175
DOI: 10.2307/2533866
Stable URL: http://www.jstor.org/stable/2533866
Page Count: 11
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Assessment of Model Adequacy for Markov Regression Time Series Models
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Abstract

In Markov regression models with time series data, we apply asymptotic results to obtain the quasi-score, quasi-Wald, and quasi-likelihood ratio tests for assessing model adequacy. Based on limited simulation studies, we show that these three test statistics, particularly the quasi-score test, perform reasonably well in small samples. In addition, we apply these tests to the mean-shift outlier model to examine outliers. The usefulness of these tests is demonstrated via the analysis of three practical examples.

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