کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1148961 957857 2011 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation
چکیده انگلیسی

Estimation and prediction in generalized linear mixed models are often hampered by intractable high dimensional integrals. This paper provides a framework to solve this intractability, using asymptotic expansions when the number of random effects is large. To that end, we first derive a modified Laplace approximation when the number of random effects is increasing at a lower rate than the sample size. Second, we propose an approximate likelihood method based on the asymptotic expansion of the log-likelihood using the modified Laplace approximation which is maximized using a quasi-Newton algorithm. Finally, we define the second order plug-in predictive density based on a similar expansion to the plug-in predictive density and show that it is a normal density. Our simulations show that in comparison to other approximations, our method has better performance. Our methods are readily applied to non-Gaussian spatial data and as an example, the analysis of the rhizoctonia root rot data is presented.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 141, Issue 11, November 2011, Pages 3564–3577
نویسندگان
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