期刊
BIOMETRICS
卷 60, 期 2, 页码 444-450出版社
WILEY
DOI: 10.1111/j.0006-341X.2004.00189.x
关键词
goodness-of-fit; hidden Markov model; model selection; multiple sclerosis; probability plot; stationary time series
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据