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Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions

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Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions. / Kitromilidou, S.; Fokianos, K.
In: Statistical Inference for Stochastic Processes, Vol. 19, No. 3, 10.2016, p. 337-361.

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

Harvard

Kitromilidou, S & Fokianos, K 2016, 'Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions', Statistical Inference for Stochastic Processes, vol. 19, no. 3, pp. 337-361. https://doi.org/10.1007/s11203-015-9131-z

APA

Kitromilidou, S., & Fokianos, K. (2016). Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions. Statistical Inference for Stochastic Processes, 19(3), 337-361. https://doi.org/10.1007/s11203-015-9131-z

Vancouver

Kitromilidou S, Fokianos K. Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions. Statistical Inference for Stochastic Processes. 2016 Oct;19(3):337-361. Epub 2015 Dec 26. doi: 10.1007/s11203-015-9131-z

Author

Kitromilidou, S. ; Fokianos, K. / Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions. In: Statistical Inference for Stochastic Processes. 2016 ; Vol. 19, No. 3. pp. 337-361.

Bibtex

@article{9ec02351a5b5482ea08cd9f54e379dde,
title = "Mallows{\textquoteright} quasi-likelihood estimation for log-linear Poisson autoregressions",
abstract = "We consider the problems of robust estimation and testing for a log-linear model with feedback for the analysis of count time series. We study inference for contaminated data with transient shifts, level shifts and additive outliers. It turns out that the case of additive outliers deserves special attention. We propose a robust method for estimating the regression coefficients in the presence of interventions. The resulting robust estimators are asymptotically normally distributed under some regularity conditions. A robust score type test statistic is also examined. The methodology is applied to real and simulated data.",
keywords = "Autocorrelation, Estimating equations , Generalized linear models , Integer valued time series , Interventions , Robust estimation ",
author = "S. Kitromilidou and K. Fokianos",
year = "2016",
month = oct,
doi = "10.1007/s11203-015-9131-z",
language = "English",
volume = "19",
pages = "337--361",
journal = "Statistical Inference for Stochastic Processes",
number = "3",

}

RIS

TY - JOUR

T1 - Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions

AU - Kitromilidou, S.

AU - Fokianos, K.

PY - 2016/10

Y1 - 2016/10

N2 - We consider the problems of robust estimation and testing for a log-linear model with feedback for the analysis of count time series. We study inference for contaminated data with transient shifts, level shifts and additive outliers. It turns out that the case of additive outliers deserves special attention. We propose a robust method for estimating the regression coefficients in the presence of interventions. The resulting robust estimators are asymptotically normally distributed under some regularity conditions. A robust score type test statistic is also examined. The methodology is applied to real and simulated data.

AB - We consider the problems of robust estimation and testing for a log-linear model with feedback for the analysis of count time series. We study inference for contaminated data with transient shifts, level shifts and additive outliers. It turns out that the case of additive outliers deserves special attention. We propose a robust method for estimating the regression coefficients in the presence of interventions. The resulting robust estimators are asymptotically normally distributed under some regularity conditions. A robust score type test statistic is also examined. The methodology is applied to real and simulated data.

KW - Autocorrelation

KW - Estimating equations

KW - Generalized linear models

KW - Integer valued time series

KW - Interventions

KW - Robust estimation

U2 - 10.1007/s11203-015-9131-z

DO - 10.1007/s11203-015-9131-z

M3 - Journal article

VL - 19

SP - 337

EP - 361

JO - Statistical Inference for Stochastic Processes

JF - Statistical Inference for Stochastic Processes

IS - 3

ER -