کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
418156 681615 2007 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities
چکیده انگلیسی

This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressive model. This model can approximate any linear autoregressive model arbitrarily closely while imposing no constraint on parameters to ensure stationarity. We establish sufficient conditions for posterior consistency in two different topologies. The proposed method is compared with the mixture of autoregressive model [Wong and Li, 2000. On a mixture autoregressive model. J. Roy. Statist. Soc. Ser. B 62(1), 91–115] and the double-kernel local linear approach [Fan et al., 1996. Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika 83, 189–206] by simulations and real examples. Our method shows excellent performances in these studies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 51, Issue 9, 15 May 2007, Pages 4424–4437
نویسندگان
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