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On binary and categorical time series models with feedback

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On binary and categorical time series models with feedback. / Moysiadis, T.; Fokianos, K.
In: Journal of Multivariate Analysis, Vol. 131, 10.2014, p. 209-228.

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

Harvard

Moysiadis, T & Fokianos, K 2014, 'On binary and categorical time series models with feedback', Journal of Multivariate Analysis, vol. 131, pp. 209-228. https://doi.org/10.1016/j.jmva.2014.07.004

APA

Moysiadis, T., & Fokianos, K. (2014). On binary and categorical time series models with feedback. Journal of Multivariate Analysis, 131, 209-228. https://doi.org/10.1016/j.jmva.2014.07.004

Vancouver

Moysiadis T, Fokianos K. On binary and categorical time series models with feedback. Journal of Multivariate Analysis. 2014 Oct;131:209-228. Epub 2014 Jul 21. doi: 10.1016/j.jmva.2014.07.004

Author

Moysiadis, T. ; Fokianos, K. / On binary and categorical time series models with feedback. In: Journal of Multivariate Analysis. 2014 ; Vol. 131. pp. 209-228.

Bibtex

@article{fc2cd0ebfa3a427394676a1b546fa79e,
title = "On binary and categorical time series models with feedback",
abstract = "We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.",
keywords = "Autocorrelation, Categorical data, Hidden Markov models, Latent process, Logistic regression, Multinomial regression, Nominal data, Prediction, Weak dependence",
author = "T. Moysiadis and K. Fokianos",
year = "2014",
month = oct,
doi = "10.1016/j.jmva.2014.07.004",
language = "English",
volume = "131",
pages = "209--228",
journal = "Journal of Multivariate Analysis",
issn = "0047-259X",
publisher = "Academic Press Inc.",

}

RIS

TY - JOUR

T1 - On binary and categorical time series models with feedback

AU - Moysiadis, T.

AU - Fokianos, K.

PY - 2014/10

Y1 - 2014/10

N2 - We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.

AB - We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.

KW - Autocorrelation

KW - Categorical data

KW - Hidden Markov models

KW - Latent process

KW - Logistic regression

KW - Multinomial regression

KW - Nominal data

KW - Prediction

KW - Weak dependence

U2 - 10.1016/j.jmva.2014.07.004

DO - 10.1016/j.jmva.2014.07.004

M3 - Journal article

VL - 131

SP - 209

EP - 228

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

SN - 0047-259X

ER -