Abstract:
:In this article, we propose a graphical technique for assessing the goodness-of-fit of a stationary hidden Markov model (HMM). We show that plots of the estimated distribution against the empirical distribution detect lack of fit with high probability for large sample sizes. By considering plots of the univariate and multidimensional distributions, we are able to examine the fit of both the assumed marginal distribution and the correlation structure of the observed data. We provide general conditions for the convergence of the empirical distribution to the true distribution, and demonstrate that these conditions hold for a wide variety of time-series models. Thus, our method allows us to compare not only the fit of different HMMs, but also that of other models as well. We illustrate our technique using a multiple sclerosis data set.
journal_name
Biometricsjournal_title
Biometricsauthors
MacKay Altman Rdoi
10.1111/j.0006-341X.2004.00189.xsubject
Has Abstractpub_date
2004-06-01 00:00:00pages
444-50issue
2eissn
0006-341Xissn
1541-0420pii
BIOM189journal_volume
60pub_type
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