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Some recent progress in count time series

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Some recent progress in count time series. / Fokianos, K.

In: Statistics:A Journal of Theoretical and Applied Statistics, Vol. 45, No. 1, 2011, p. 49-58.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Fokianos, K 2011, 'Some recent progress in count time series', Statistics:A Journal of Theoretical and Applied Statistics, vol. 45, no. 1, pp. 49-58. https://doi.org/10.1080/02331888.2010.541250

APA

Fokianos, K. (2011). Some recent progress in count time series. Statistics:A Journal of Theoretical and Applied Statistics, 45(1), 49-58. https://doi.org/10.1080/02331888.2010.541250

Vancouver

Fokianos K. Some recent progress in count time series. Statistics:A Journal of Theoretical and Applied Statistics. 2011;45(1):49-58. https://doi.org/10.1080/02331888.2010.541250

Author

Fokianos, K. / Some recent progress in count time series. In: Statistics:A Journal of Theoretical and Applied Statistics. 2011 ; Vol. 45, No. 1. pp. 49-58.

Bibtex

@article{440b0499b1054ac3a2a8d655850ab10a,
title = "Some recent progress in count time series",
abstract = "We review some regression models for the analysis of count time series. These models have been the focus of several investigations over the last years, but only recently simple conditions for stationarity and ergodicity were worked out in detail. This advancement makes possible the development of the maximum-likelihood estimation theory under minimal assumptions.",
keywords = "autocorrelation, covariates, ergodicity, generalized linear models, perturbation, prediction, stationarity, volatility",
author = "K. Fokianos",
year = "2011",
doi = "10.1080/02331888.2010.541250",
language = "English",
volume = "45",
pages = "49--58",
journal = "Statistics:A Journal of Theoretical and Applied Statistics",
issn = "0233-1888",
publisher = "Taylor & Francis",
number = "1",

}

RIS

TY - JOUR

T1 - Some recent progress in count time series

AU - Fokianos, K.

PY - 2011

Y1 - 2011

N2 - We review some regression models for the analysis of count time series. These models have been the focus of several investigations over the last years, but only recently simple conditions for stationarity and ergodicity were worked out in detail. This advancement makes possible the development of the maximum-likelihood estimation theory under minimal assumptions.

AB - We review some regression models for the analysis of count time series. These models have been the focus of several investigations over the last years, but only recently simple conditions for stationarity and ergodicity were worked out in detail. This advancement makes possible the development of the maximum-likelihood estimation theory under minimal assumptions.

KW - autocorrelation

KW - covariates

KW - ergodicity

KW - generalized linear models

KW - perturbation

KW - prediction

KW - stationarity

KW - volatility

U2 - 10.1080/02331888.2010.541250

DO - 10.1080/02331888.2010.541250

M3 - Journal article

VL - 45

SP - 49

EP - 58

JO - Statistics:A Journal of Theoretical and Applied Statistics

JF - Statistics:A Journal of Theoretical and Applied Statistics

SN - 0233-1888

IS - 1

ER -