scholarly journals Analysis of Factors Affecting Leprosy Cases in East Java Province with Spatial Autoregressive Model (SAR)

2021 ◽  
Vol 7 (1) ◽  
pp. 59
Author(s):  
Desie Rahmawati ◽  
Hardian Bimanto
Keyword(s):  
Autoregressive Model ◽  
Secondary Data ◽  
Spatial Autoregressive Model ◽  
Male Population ◽  
Factors Affecting ◽  
Observation Units ◽  
Spatial Autoregressive ◽  
The Mean ◽  
Index Value ◽  
Healthy Housing

Leprosy caused by Mycobacterium leprae is still a health problem in Indonesia. The incidence of leprosy in every regency/city in East Java Province indicates a spatial dependence. This study aims to describe the pattern of leprosy incidence and identify factors that influence the incidence of leprosy in East Java Province. This study used secondary data published by East Java Provincial Health Office and East Java Central Statistics Agency in 2018. The observation units in this study are 38 regencies/cities in East Java Province. The analytical method used is Spatial Autoregressive Model (SAR) which is a spatial approach based on area. Based on the results of analysis show that Moran's index value = 0.250 (p = 0.018) which means indicating a spatial dependency. The mean years' schools (p = 0.001) and the male population (p = 0.006) had a significant effect on the incidence of leprosy. Meanwhile, the percentage of healthy housing coverage (p = 0.111) and population density (p = 0.055) did not affect the incidence of leprosy. The spread pattern of leprosy in East Java Province is clustered in adjacent areas and factors that affect the incidence of leprosy are the mean years' schools and the male population.

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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