کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1156211 958810 2008 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Asymptotic properties of particle filter-based maximum likelihood estimators for state space models
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات (عمومی)
پیش نمایش صفحه اول مقاله
Asymptotic properties of particle filter-based maximum likelihood estimators for state space models
چکیده انگلیسی

We study the asymptotic performance of approximate maximum likelihood estimators for state space models obtained via sequential Monte Carlo methods. The state space of the latent Markov chain and the parameter space are assumed to be compact. The approximate estimates are computed by, firstly, running possibly dependent particle filters on a fixed grid in the parameter space, yielding a pointwise approximation of the log-likelihood function. Secondly, extensions of this approximation to the whole parameter space are formed by means of piecewise constant functions or B-spline interpolation, and approximate maximum likelihood estimates are obtained through maximization of the resulting functions. In this setting we formulate criteria for how to increase the number of particles and the resolution of the grid in order to produce estimates that are consistent and asymptotically normal.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Stochastic Processes and their Applications - Volume 118, Issue 4, April 2008, Pages 649–680
نویسندگان
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