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:: Volume 7, Issue 1 (9-2013) ::
JSS 2013, 7(1): 103-124 Back to browse issues page
Spatial Analysis of Structured Additive Regression and Modeling of Crime Data in Tehran City Using Integrated Nested Laplace Approximation
Kobra Gholizadeh * , Mohsen Mohammadzadeh , Zahra Ghayyomi
Abstract:   (15683 Views)
In Bayesian analysis of structured additive regression models which are a flexible class of statistical models, the posterior distributions are not available in a closed form, so Markov chain Monte Carlo algorithm due to complexity and large number of hyperparameters takes long time. Integrated nested Laplace approximation method can avoid the hard simulations using the Gaussian and Laplace approximations. In this paper, consideration of spatial correlation of the data in structured additive regression model and its estimation by the integrated nested Laplace approximation are studied. Then a crime data set in Tehran city are modeled and evaluated. Next, a simulation study is performed to compare the computational time and precision of the models provided by the integrated nested Laplace approximation and Markov chain Monte Carlo algorithm
Keywords: Structured Additive Regression Model, Integrated Nested Laplace Approximation, Markov Chain Monte Carlo
Full-Text [PDF 524 kb]   (3011 Downloads)    
Type of Study: Applied | Subject: Spatial Statistics
Received: 2013/04/22 | Accepted: 2013/12/29 | Published: 2013/12/29
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Gholizadeh K, Mohammadzadeh M, Ghayyomi Z. Spatial Analysis of Structured Additive Regression and Modeling of Crime Data in Tehran City Using Integrated Nested Laplace Approximation. JSS 2013; 7 (1) :103-124
URL: http://jss.irstat.ir/article-1-204-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 1 (9-2013) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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