کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6870466 | 681394 | 2014 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reversible jump MCMC for nonparametric drift estimation for diffusion processes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Reversible jump MCMC for nonparametric drift estimation for diffusion processes Reversible jump MCMC for nonparametric drift estimation for diffusion processes](/preview/png/6870466.png)
چکیده انگلیسی
In the context of nonparametric Bayesian estimation a Markov chain Monte Carlo algorithm is devised and implemented to sample from the posterior distribution of the drift function of a continuously or discretely observed one-dimensional diffusion. The drift is modeled by a scaled linear combination of basis functions with a Gaussian prior on the coefficients. The scaling parameter is equipped with a partially conjugate prior. The number of basis functions in the drift is equipped with a prior distribution as well. For continuous data, a reversible jump Markov chain algorithm enables the exploration of the posterior over models of varying dimension. Subsequently, it is explained how data-augmentation can be used to extend the algorithm to deal with diffusions observed discretely in time. Some examples illustrate that the method can give satisfactory results. In these examples a comparison is made with another existing method as well.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 71, March 2014, Pages 615-632
Journal: Computational Statistics & Data Analysis - Volume 71, March 2014, Pages 615-632
نویسندگان
Frank van der Meulen, Moritz Schauer, Harry van Zanten,