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- 저자 : Lu Feilong (검색결과 2 건)
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Empirical likelihood confidence regions for autoregressive models with explanatory variables
Lu Feilong,Wang Dehui 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.3
The empirical likelihood method is very useful for establishing confidence region of the parameters of interest. In this paper, an empirical likelihood confidence region for the parameters of a univariate AR(p) model with a single explanatory variable which enters in the model through a nonlinear function is studied. Since the analytical expression of the nonlinear function is unknown, it is replaced by a nonparametric estimator, which is subsequently plugged in the estimating equations for the parameters of interest (the autoregressive parameters) to obtain the empirical likelihood confidence region. Our approach is to establish an empirical likelihood ratio statistic which is asymptotically chi-squared distributed. Some simulation results are also presented to illustrate the performance of the empirical likelihood method. An application to a real data set is provided.
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Flexible INAR(1) models for equidispersed, underdispersed or overdispersed counts
Kang Yao,Wang Dehui,Lu Feilong,Wang Shuhui 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.4
Equidispersed, underdispersed and overdispersed count data are commonly encountered in practice. To better describe these data characteristics, this paper develops two classes of INAR(1) processes, which not only can model a wide range of overdispersion and underdispersion, but also have ability to describe the zero-inflated and zero-deflated characteristics of the count data. The probabilistic and statistical properties of the two processes are studied. Estimators of the model parameters are derived by using conditional maximum likelihood (CML) and modified conditional least squares (MCLS) methods. Some asymptotic properties and numerical results of the estimators are investigated. Three real examples are given to show the flexibility and usefulness of the proposed models.
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