APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA

Spatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables. The nonparametric regression method is better suited for data...

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Main Authors: Nindi Pigitha, Anik Djuraidah, Aji Hamim Wigena
Format: Article
Language:English
Published: Universitas Pattimura 2023-04-01
Series:Barekeng
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Online Access:https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7683
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author Nindi Pigitha
Anik Djuraidah
Aji Hamim Wigena
author_facet Nindi Pigitha
Anik Djuraidah
Aji Hamim Wigena
author_sort Nindi Pigitha
collection DOAJ
description Spatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables. The nonparametric regression method is better suited for data with a nonlinear connection because it does not need linear assumptions. One of the nonparametric regression methods is penalized spline regression (P-Spline). The P-spline has a simple mathematical relationship with mixed linear model. The use of a mixed linear model allows the P-Spline to be combined with other statistical models. PS-SAR is a combination of the P-Spline and the SAR spatial model so that it can analyze spatial data with a semiparametric approach. Based on data from monitoring the development of the HIV situation in 2018, the number of HIV cases in Indonesia shows a clustered pattern that indicate spatial dependence. In addition, the relationship between the number of positive cases and the factors tends to be nonlinear. Therefore, this study aims to apply the PS-SAR model to HIV case data in Indonesia. The resulting model is evaluated based on the estimates of autoregressive spatial coefficient, MSE, MAPE, and Pseudo R2. Based on the results, the PS-SAR model has an autoregressive spatial coefficient similar to the SAR model and has smaller MSE and MAPE than the SAR model.
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spelling doaj-art-b1e0a8d91cbb418bb8d4f10c8dc7c8c02025-08-20T03:35:56ZengUniversitas PattimuraBarekeng1978-72272615-30172023-04-011710527053410.30598/barekengvol17iss1pp0527-05347683APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIANindi Pigitha0Anik Djuraidah1Aji Hamim Wigena2Department of Statistics, Faculty Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty Mathematics and Natural Sciences, IPB University, IndonesiaSpatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables. The nonparametric regression method is better suited for data with a nonlinear connection because it does not need linear assumptions. One of the nonparametric regression methods is penalized spline regression (P-Spline). The P-spline has a simple mathematical relationship with mixed linear model. The use of a mixed linear model allows the P-Spline to be combined with other statistical models. PS-SAR is a combination of the P-Spline and the SAR spatial model so that it can analyze spatial data with a semiparametric approach. Based on data from monitoring the development of the HIV situation in 2018, the number of HIV cases in Indonesia shows a clustered pattern that indicate spatial dependence. In addition, the relationship between the number of positive cases and the factors tends to be nonlinear. Therefore, this study aims to apply the PS-SAR model to HIV case data in Indonesia. The resulting model is evaluated based on the estimates of autoregressive spatial coefficient, MSE, MAPE, and Pseudo R2. Based on the results, the PS-SAR model has an autoregressive spatial coefficient similar to the SAR model and has smaller MSE and MAPE than the SAR model.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7683human immunodeficiency virusnonlinearspatial autoregressive modelsemiparametricspenalized splinepenalized spline-spatial autoregressive model
spellingShingle Nindi Pigitha
Anik Djuraidah
Aji Hamim Wigena
APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
Barekeng
human immunodeficiency virus
nonlinear
spatial autoregressive model
semiparametrics
penalized spline
penalized spline-spatial autoregressive model
title APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
title_full APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
title_fullStr APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
title_full_unstemmed APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
title_short APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
title_sort application of penalized spline spatial autoregressive model to hiv case data in indonesia
topic human immunodeficiency virus
nonlinear
spatial autoregressive model
semiparametrics
penalized spline
penalized spline-spatial autoregressive model
url https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7683
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