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A general goodness-of-fit test for Markov and hidden Markov models.

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A general goodness-of-fit test for Markov and hidden Markov models. / Titman, Andrew C.; Sharples, Linda D.
In: Statistics in Medicine, Vol. 27, No. 12, 30.05.2008, p. 2177-2195.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Titman, AC & Sharples, LD 2008, 'A general goodness-of-fit test for Markov and hidden Markov models.', Statistics in Medicine, vol. 27, no. 12, pp. 2177-2195. https://doi.org/10.1002/sim.3033

APA

Titman, A. C., & Sharples, L. D. (2008). A general goodness-of-fit test for Markov and hidden Markov models. Statistics in Medicine, 27(12), 2177-2195. https://doi.org/10.1002/sim.3033

Vancouver

Titman AC, Sharples LD. A general goodness-of-fit test for Markov and hidden Markov models. Statistics in Medicine. 2008 May 30;27(12):2177-2195. doi: 10.1002/sim.3033

Author

Titman, Andrew C. ; Sharples, Linda D. / A general goodness-of-fit test for Markov and hidden Markov models. In: Statistics in Medicine. 2008 ; Vol. 27, No. 12. pp. 2177-2195.

Bibtex

@article{0294b4194b064f9d975ff2309079a54f,
title = "A general goodness-of-fit test for Markov and hidden Markov models.",
abstract = "Markov models are a convenient and useful method of estimating transition rates between levels of a categorical response variable, such as a disease stage, which changes over time. In medical applications the response variable is typically observed at irregular intervals. A Pearson-type goodness-of-fit test for such models was proposed by Aguirre-Hernandez and Farewell (Statistics in Medicine. 2002), but this test is not applicable in the common situation where the process includes an absorbing state, such as death, for which the time of entry is known precisely nor when the data include censored state observations. This paper presents a modification to the Pearson-type test to allow for these cases. An extension of the method, to allow for the class of hidden Markov models where the response variable is subject to misclassification error, is given. The method is applied to data on cardiac allograft vasculopathy in post-heart-transplant patients.",
author = "Titman, {Andrew C.} and Sharples, {Linda D.}",
year = "2008",
month = may,
day = "30",
doi = "10.1002/sim.3033",
language = "English",
volume = "27",
pages = "2177--2195",
journal = "Statistics in Medicine",
issn = "1097-0258",
publisher = "John Wiley and Sons Ltd",
number = "12",

}

RIS

TY - JOUR

T1 - A general goodness-of-fit test for Markov and hidden Markov models.

AU - Titman, Andrew C.

AU - Sharples, Linda D.

PY - 2008/5/30

Y1 - 2008/5/30

N2 - Markov models are a convenient and useful method of estimating transition rates between levels of a categorical response variable, such as a disease stage, which changes over time. In medical applications the response variable is typically observed at irregular intervals. A Pearson-type goodness-of-fit test for such models was proposed by Aguirre-Hernandez and Farewell (Statistics in Medicine. 2002), but this test is not applicable in the common situation where the process includes an absorbing state, such as death, for which the time of entry is known precisely nor when the data include censored state observations. This paper presents a modification to the Pearson-type test to allow for these cases. An extension of the method, to allow for the class of hidden Markov models where the response variable is subject to misclassification error, is given. The method is applied to data on cardiac allograft vasculopathy in post-heart-transplant patients.

AB - Markov models are a convenient and useful method of estimating transition rates between levels of a categorical response variable, such as a disease stage, which changes over time. In medical applications the response variable is typically observed at irregular intervals. A Pearson-type goodness-of-fit test for such models was proposed by Aguirre-Hernandez and Farewell (Statistics in Medicine. 2002), but this test is not applicable in the common situation where the process includes an absorbing state, such as death, for which the time of entry is known precisely nor when the data include censored state observations. This paper presents a modification to the Pearson-type test to allow for these cases. An extension of the method, to allow for the class of hidden Markov models where the response variable is subject to misclassification error, is given. The method is applied to data on cardiac allograft vasculopathy in post-heart-transplant patients.

U2 - 10.1002/sim.3033

DO - 10.1002/sim.3033

M3 - Journal article

VL - 27

SP - 2177

EP - 2195

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 12

ER -