Robust estimation methods for a class of log-linear count time series models

2015 ◽  
Vol 86 (4) ◽  
pp. 740-755 ◽  
Author(s):  
Stella Kitromilidou ◽  
Konstantinos Fokianos
Keyword(s):  
Time Series ◽  
Robust Estimation ◽  
Estimation Methods ◽  
Time Series Models ◽  
Count Time Series ◽  
Log Linear

Zero-inflated count time series models using Gaussian copula

2019 ◽  
Vol 38 (3) ◽  
pp. 342-357 ◽  
Author(s):  
Mohammed Alqawba ◽  
Norou Diawara ◽  
N. Rao Chaganty
Keyword(s):  
Time Series ◽  
Gaussian Copula ◽  
Time Series Models ◽  
Count Time Series

SUPERPOSITIONED STATIONARY COUNT TIME SERIES

2019 ◽  
pp. 1-19
Author(s):  
Yisu Jia ◽  
Robert Lund ◽  
James Livsey
Keyword(s):  
Time Series ◽  
Negative Binomial ◽  
Stationary Sequence ◽  
Time Series Models ◽  
Count Time Series ◽  
Marginal Distributions ◽  
Autocovariance Function ◽  
Basic Properties ◽  
Model Class ◽  
Discrete Uniform

Abstract This paper probabilistically explores a class of stationary count time series models built by superpositioning (or otherwise combining) independent copies of a binary stationary sequence of zeroes and ones. Superpositioning methods have proven useful in devising stationary count time series having prespecified marginal distributions. Here, basic properties of this model class are established and the idea is further developed. Specifically, stationary series with binomial, Poisson, negative binomial, discrete uniform, and multinomial marginal distributions are constructed; other marginal distributions are possible. Our primary goal is to derive the autocovariance function of the resulting series.

Moment structures of parameter-driven count time series models

2017 ◽  
Author(s):  
Nawwal Ahmad Bukhari ◽  
Koh You Beng ◽  
Ibrahim Mohamed
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Count Time Series

scholarly journals On Outliers and Interventions in Count Time Series following GLMs

2014 ◽  
Vol 43 (3) ◽  
pp. 181-193 ◽  
Author(s):  
Roland Fried ◽  
Tobias Liboschik ◽  
Hanan Elsaied ◽  
Stella Kitromilidou ◽  
Konstantinos Fokianos
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Robust Estimation ◽  
Linear Models ◽  
Sequential Testing ◽  
Generalised Linear Models ◽  
Count Time Series ◽  
Different Types ◽  
Robust Parameter ◽  
Robust Parameter Estimation

We discuss the analysis of count time series following generalised linear models in the presence of outliers and intervention effects. Different modifications of such models are formulated which allow to incorporate, detect and to a certain degree distinguish extraordinary events (interventions) of different types in count time series retrospectively. An outlook on extensions to the problem of robust parameter estimation, identification of the model orders by robust estimation of autocorrelations and partial autocorrelations, and online surveillance by sequential testing for outlyingness is provided. 

Sign in / Sign up

Export Citation Format

Share Document