A Bayesian semi-nonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The semi-nonparametric nature of the approach allows for the relaxation of the assumption of normal errors. An application and a small Monte Carlo study indicate that the methods we advocate are both feasible and necessary.
机构:
Univ Chile, Fac Med, Div Bioestadist, Escuela Salud Publ, Santiago 7, ChileUniv Chile, Fac Med, Div Bioestadist, Escuela Salud Publ, Santiago 7, Chile
Gutierrez, Luis
Quintana, Fernando A.
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Pontificia Univ Catolica Chile, Fac Matemat, Dept Estadist, Santiago, ChileUniv Chile, Fac Med, Div Bioestadist, Escuela Salud Publ, Santiago 7, Chile
机构:
Chinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R ChinaChinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China
Kang, Kai
Song, Xinyuan
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Chinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China
Chinese Univ Hong Kong, Shenzhen Res Inst, Shatin, Hong Kong, Peoples R ChinaChinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China
Song, Xinyuan
Hu, X. Joan
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Simon Fraser Univ, Dept Stat & Actuarial Sci, Burnaby, BC, CanadaChinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China
Hu, X. Joan
Zhu, Hongtu
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机构:
Univ N Carolina, Dept Biostat, Chapel Hill, NC 27515 USA
Univ N Carolina, Biomed Res Imaging Ctr, Chapel Hill, NC 27515 USAChinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China