scholarly journals ON THE MODELLING OF LEPROSY PREVALENCE IN SOUTH SULAWESI USING SPATIAL AUTOREGRESSIVE MODEL

2020 ◽  
Vol 4 (2) ◽  
pp. 245-253
Author(s):  
Rezki Melany Sabil ◽  
Ray Sastri
Keyword(s):  
Spatial Autocorrelation ◽  
Autoregressive Model ◽  
Healthy Lifestyle ◽  
District Level ◽  
Spatial Autoregressive Model ◽  
Ministry Of Health ◽  
South Sulawesi ◽  
High Prevalence ◽  
Leprosy Case ◽  
Spatial Autoregressive

The prevalence of leprosy is the number of leprosy cases per 10.000 peoples. Based on data from the Ministry of Health, the highest prevalenece of leprosy was in South Sulawesi. This is needs a special attention because leprosy is a contagious disease. The number of leprosy cases in an area may be influenced by the number of leprosy case in the neighbor area due to the movement of the air. So that, the location of  area need to be included in analysis of leprosy. The aim of this study is to identify the variables that spatially affect the prevalence of leprosy in South Sulawesi and modelling it. This study uses data from the Ministry of Health for year 2016.  The method of analysis is Spatial Autoregressive Model (SAR).  The results is There is a positive spatial autocorrelation in the prevalence of leprosy in district level, which means that regions with high prevalence of leprosy are surrounded by areas with high prevalence of leprosy, and vice versa. The prevalence of leprosy in an area is influenced by the prevalence of leprosy in neighbor districts, the percentage of BCG vaccines recipient and the percentage of households with healthy lifestyle.

scholarly journals Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1448
Author(s):  
Xuan Liu ◽  
Jianbao Chen
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Autoregressive Model ◽  
Monte Carlo Studies ◽  
Spatial Autoregressive ◽  
Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

scholarly journals Estimation of Partially Specified Spatial Autoregressive Model

2014 ◽  
Vol 2 (3) ◽  
pp. 226-235
Author(s):  
Yuanqing Zhang
Keyword(s):  
Autoregressive Model ◽  
Instrumental Variable ◽  
Error Term ◽  
Sufficient Conditions ◽  
Spatial Autoregressive Model ◽  
Dimensional Parameter ◽  
Instrumental Variable Estimation ◽  
Weighting Matrix ◽  
Finite Dimensional ◽  
Spatial Autoregressive

Abstract In this paper, we study estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, we propose an instrumental variable estimation. Under some sufficient conditions, we show that the proposed estimator for the finite dimensional parameter is root-n consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and also asymptotically distributed though at a rate slower than root-n. Monte Carlo simulations verify our theory and the results suggest that the proposed method has some practical value.

A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model

1982 ◽  
Vol 14 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
L Anselin
Keyword(s):  
Monte Carlo Simulation ◽  
Autoregressive Model ◽  
Ad Hoc ◽  
Simulation Experiment ◽  
Small Sample ◽  
Bayesian Estimator ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Small Sample Properties ◽  
Spatial Autoregressive

This note considers a Bayesian estimator and an ad hoc procedure for the parameters of a first-order spatial autoregressive model. The approaches are derived, and their small sample properties compared by means of a Monte Carlo simulation experiment.

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