This paper proposes a profile empirical likelihood for the partial parameters in ARMA(p, q) models with infinite variance. We introduce a smoothed empirical log-likelihood ratio statistic. Also, the paper proves a nonparametric version of Wilks's theorem. Furthermore, we conduct a simulation to illustrate the performance of the proposed method.
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Kookmin Univ, Sch Business Adm, Seoul, South KoreaKookmin Univ, Sch Business Adm, Seoul, South Korea
Chung, Yeojin
Rabe-Hesketh, Sophia
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Univ Calif Berkeley, Grad Sch Educ, Berkeley, CA 94720 USA
Univ London, Inst Educ, London WC1E 7HU, EnglandKookmin Univ, Sch Business Adm, Seoul, South Korea
Rabe-Hesketh, Sophia
Dorie, Vincent
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Columbia Univ, Dept Stat, New York, NY 10027 USAKookmin Univ, Sch Business Adm, Seoul, South Korea
Dorie, Vincent
Gelman, Andrew
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Columbia Univ, Dept Stat, New York, NY 10027 USAKookmin Univ, Sch Business Adm, Seoul, South Korea
Gelman, Andrew
Liu, Jingchen
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Columbia Univ, Dept Stat, New York, NY 10027 USAKookmin Univ, Sch Business Adm, Seoul, South Korea
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Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China
Wong, Heung
Liu, Feng
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Chongqing Inst Technol, Dept Stat, Chongqing 400050, Peoples R ChinaHong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China
Liu, Feng
Chen, Min
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Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R ChinaHong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China
Chen, Min
Ip, Wai Cheung
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Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China