Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.

Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.

Several classes of models have been proposed to describe the spatial processes. A special class of non-separable spatial unilateral model for modeling spatial data on two dimensional rectangular regular grid is the spatial autoregressive model, denoted by AR( PI ,1). In this paper, we establish a pr...

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Main Authors: Awang, Norhashidah, Shitan, Mahendran
Format: Conference or Workshop Item
Published: 2008
Subjects:
QA1-939 Mathematics
Online Access:http://eprints.usm.my/11688/
http://eprints.usm.my/11688/1/MAXIMUM_LIKELIHOOD_ESTIMATION_FOR_THE_NON%2DSEPARABLE_SPATIAL_UNILATERAL_AUTOREGRESSIVE_MODEL.pdf
id usm-11688
recordtype eprints
spelling usm-116882013-07-13T04:46:08Z Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model. Awang, Norhashidah Shitan, Mahendran QA1-939 Mathematics Several classes of models have been proposed to describe the spatial processes. A special class of non-separable spatial unilateral model for modeling spatial data on two dimensional rectangular regular grid is the spatial autoregressive model, denoted by AR( PI ,1). In this paper, we establish a procedure to estimate the parameters of this model using the maximum likelihood method. We show that this procedure is practical and easy to be implemented We illustrate the procedure by fitting the AR(l, J) model to two set of data on the regular rectangular grid. 2008-08 Conference or Workshop Item PeerReviewed application/pdf http://eprints.usm.my/11688/1/MAXIMUM_LIKELIHOOD_ESTIMATION_FOR_THE_NON%2DSEPARABLE_SPATIAL_UNILATERAL_AUTOREGRESSIVE_MODEL.pdf Awang, Norhashidah and Shitan, Mahendran (2008) Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model. In: The 3rd International Conference on Mathematics and Statistics, 5-6 August 2008, Institut Pertanian Bogar, Indonesia. http://eprints.usm.my/11688/
institution Universiti Sains Malaysia
Universiti Sains Malaysia
repository_type Digital Repository
institution_category Local University
building USM Repository
collection Online Access
topic QA1-939 Mathematics
spellingShingle QA1-939 Mathematics
Awang, Norhashidah
Shitan, Mahendran
Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
description Several classes of models have been proposed to describe the spatial processes. A special class of non-separable spatial unilateral model for modeling spatial data on two dimensional rectangular regular grid is the spatial autoregressive model, denoted by AR( PI ,1). In this paper, we establish a procedure to estimate the parameters of this model using the maximum likelihood method. We show that this procedure is practical and easy to be implemented We illustrate the procedure by fitting the AR(l, J) model to two set of data on the regular rectangular grid.
format Conference or Workshop Item
author Awang, Norhashidah
Shitan, Mahendran
author_facet Awang, Norhashidah
Shitan, Mahendran
author_sort Awang, Norhashidah
title Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
title_short Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
title_full Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
title_fullStr Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
title_full_unstemmed Maximum; Likelihood Estimation For The Non-Separable Spatial Unilateral Autoregressive Model.
title_sort maximum; likelihood estimation for the non-separable spatial unilateral autoregressive model.
publishDate 2008
url http://eprints.usm.my/11688/
http://eprints.usm.my/11688/1/MAXIMUM_LIKELIHOOD_ESTIMATION_FOR_THE_NON%2DSEPARABLE_SPATIAL_UNILATERAL_AUTOREGRESSIVE_MODEL.pdf
first_indexed 2018-09-08T05:31:24Z
last_indexed 2018-09-08T05:31:24Z
_version_ 1611016106965729280

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