Assessment of model adequacy for Markov regression time series models.
In Markov regression models with time series data, we apply asymptomatic 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.
Léon
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LF
Environmental Risk Analysis, San Mateo, CA 94402-2512, USA.
Tsai
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CL
D000328:Adult; D000818:Animals; D001699:Biometry / Q000379:methods*; D001723:Birth Rate; D002140:California / Q000453:epidemiology; D005260:Female; D000882:Haplorhini; D006258:Head and Neck Neoplasms / Q000401:mortality; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008423:Maternal Age; D015233:Models, Statistical; D011247:Pregnancy; D018566:Pregnancy, High-Risk; D012044:Regression Analysis; D009435:Synaptic Transmission; D013997:Time Factors