Seasonal count time series

被引:7
|
作者
KONG, J. I. A. J. I. E. [1 ]
LUND, R. O. B. E. R. T. [1 ]
机构
[1] Univ Calif Santa Cruz, Dept Stat, Santa Cruz, CA 95064 USA
关键词
Copulas; count time series; PARMA series; particle filtering; periodicities; SARMA series; AUTOREGRESSIVE PROCESSES; MODEL; COMPUTATION; SIMULATION; PREDICTION; REGRESSION;
D O I
10.1111/jtsa.12651
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This article uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm. The model constructed here permits any marginal distribution for the series and the most flexible autocorrelations possible, including those with negative dependence. Likelihood methods of inference are explored. The article first develops the modeling methods, which entail a discrete transformation of a Gaussian process having seasonal dynamics. Properties of this model class are then established and particle filtering likelihood methods of parameter estimation are developed. A simulation study demonstrating the efficacy of the methods is presented and an application to the number of rainy days in successive weeks in Seattle, Washington is given.
引用
收藏
页码:93 / 124
页数:32
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