A NEW INTEGER-VALUED AUTO-REGRESSIVE MODEL BASED ON BINOMIAL THINNING OPERATOR
The integer-valued time series appears in many areas of real life,for example,medical care,economics,transportation,insurance,finance,and so on.Especially,we are in the big data period and all kinds of dispersed integer-valued data may appear everyday.The thinning operator is the main method to study the integer-valued time series.In this paper,we propose a new stationary integer-valued first-order auto-regressive(INAR(1))model based on binomial thinning operator.The model is constructed by considering the innovation to fit different dispersed data.The definition and statistical properties of the model are given.The maximum likelihood method is applied to estimate the parameters.We considered one specific model,in which the innovation follows geometric distribution,which can deal with over-dispersed integer-valued time series data.The performance of CML estimators are evaluated by simulation study.An application is given to an offence data in Track 1706 in Pittsburgh.
Binomial thinning operator Over-dispersed Parameter estimation Geometric distribution
Qingchun Zhang Xiaodong Fan
School of science,Jilin Institute of Chemical Technology,45 Chengde Street,Jilin City,China
国际会议
吉林
英文
347-350
2018-06-23（万方平台首次上网日期，不代表论文的发表时间）