Nonparametric spatial regression with spatial autoregressive error structure

Statistics ◽  
2015 ◽  
Vol 50 (1) ◽  
pp. 60-75
Author(s):  
Hongxia Wang ◽  
Jinguan Lin ◽  
Jinde Wang
Keyword(s):  
Spatial Regression ◽  
Error Structure ◽  
Spatial Autoregressive

scholarly journals Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Oluyemi A. Okunlola ◽  
Mohannad Alobid ◽  
Olusanya E. Olubusoye ◽  
Kayode Ayinde ◽  
Adewale F. Lukman ◽  
...  
Keyword(s):  
Spatial Model ◽  
Spatial Regression ◽  
Spatial Models ◽  
Cold Spot ◽  
Precipitation Data ◽  
Error Structure ◽  
Robust Approach ◽  
Spatial Linear Model ◽  
Spatial Autoregressive ◽  
Definition Of

AbstractIn this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

scholarly journals PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017

2020 ◽  
Vol 4 (1) ◽  
pp. 164-178
Author(s):  
Hardani Prisma Rizky ◽  
Wara Pramesti ◽  
Gangga Anuraga
Keyword(s):  
Moving Average ◽  
Spatial Regression ◽  
P Value ◽  
Autoregressive Moving Average ◽  
Test Results ◽  
Tuberculosis Patients ◽  
Weighting Matrix ◽  
Regression Approach ◽  
Spatial Dependencies ◽  
Spatial Autoregressive

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

scholarly journals Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Tobias Rüttenauer
Keyword(s):  
Monte Carlo ◽  
Spatial Data ◽  
Regression Models ◽  
Spatial Regression ◽  
Spillover Effects ◽  
Spatial Error ◽  
Monte Carlo Experiments ◽  
Spatial Autoregressive ◽  
Spatial Regression Models ◽  
Variable Bias

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive and spatial error specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) and the simple spatial lag of X (SLX) provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect “spillover” effects, several—quite realistic—situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.

scholarly journals METODE SPATIAL AUTOREGRESSIVE DALAM MEMODELKAN MASYARAKAT YANG BERPERILAKU MCK DI SUNGAI

2017 ◽  
Vol 6 (4) ◽  
pp. 233
Author(s):  
KOMANG KOKOM SUCAHYATI DEWI P ◽  
MADE SUSILAWATI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Autoregressive Process ◽  
Spatial Effect ◽  
Spatial Autoregressive

Spatial autoregressive (SAR) is a model of spatial regression which assumes that autoregressive process is only for the dependent variable by considering the spatial effect. The spatial effect consists of spatial dependence and spatial heterogeneity. One of problems which considers spatial effect is the case of society which do public bathing, washing, and toilets facilities (PBWTF) in the river, in Blahbatuh District. The aim of this research is to obtaine the model of society which still do PBWTF in the river, in Blahbatuh Districts in 2016 and to determine the factors that influence it. In this research, we obtained the model which is able to illustrate the case of society which do PBWTF and the factors that influence it such as the amount of householde which do not have latrine and the amount of family who lives near the river in every subvillage in Blahbatuh District.   Keywords: SAR, Spatial Effect, and Public Bathing, Washing, and Toilets Facilities in the River.  

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAH TAHUN 2017 MENGGUNAKAN ANALISIS REGRESI SPASIAL

2020 ◽  
Vol 11 (1) ◽  
pp. 45
Author(s):  
Wahyuni Alwi ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Models ◽  
Health Workers ◽  
Moving Average ◽  
Spatial Regression ◽  
Influence Factors ◽  
P Value ◽  
Linear Regression Models ◽  
Spatial Error ◽  
Central Java ◽  
Spatial Autoregressive

This research discussed about model of Human Development Index (HDI) in Central Java with spatial regression analysis. and identify  variables that give significant influence. First, analyze the influence factors based on result of p-value from t test in multiple linear regression models. Then, made spatial weight matrix with queen continguity method. After that, estimate spatial regression models, namely spatial autoregressive (SAR), Spatial error models (SEM), and spatial autoregive moving average (SARMA) and  choose the best model based on minimum AIC value. The results showed that SAR was the best spatial regression model and the significant variables was the gross enrollment rates at senior high schools, the health workers, and the district minimum wages. All of them that give positive influences. The variable that give biggest influence for HDI was the health workers. Full Article

scholarly journals MEMODELKAN ANGKA GIZI BURUK DI PROVINSI BALI DENGAN PENDEKATAN REGRESI SPASIAL

2021 ◽  
Vol 10 (2) ◽  
pp. 103
Author(s):  
ANAK AGUNG ISTRI AYU PRATAMI ◽  
I KOMANG GDE SUKARSA ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Birth Weight ◽  
Exclusive Breastfeeding ◽  
Spatial Regression ◽  
Geographic Location ◽  
Regression Method ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Error ◽  
Regression Methods ◽  
Spatial Autoregressive

Nutritional problems in toddler are still a serious problem in various districts/cities in Indonesia. The case of malnutrition in Bali Province vary in many regions and hypothesized to be influenced by geographic location, which is often known as spatial heterogeneity. To overcome this problem, a spatial regression method is used on this research. This study aims to model the factors that are hypothesized affect malnourished toddlers in Bali Province using spatial regression methods, i.e. spatial autoregressive model (SAR) and spatial error model (SEM). Both models have 5 predictors variable, i.e. the percentage of toddlers aged between 6 - 59 months who received vitamin A, the percentage of babies with low birth weight (LBW), the percentage of households with clean and healthy living behavior (PHBS), the percentage of children under five receiving exclusive breastfeeding, and the percentage of toddler health services, which are obtained from Bali Provincial Health Office. The results showed SEM method produced smaller AIC value and higher , with  and  AIC values ??of 96.24% and 60.84, respectively.

scholarly journals The Sensitivity of Spatial Regression Models to Network Misspecification

2019 ◽  
Vol 28 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Sebastian Juhl
Keyword(s):  
Bayesian Model Averaging ◽  
Spatial Regression ◽  
Model Averaging ◽  
Dependence Structure ◽  
Spatial Econometric ◽  
Replication Studies ◽  
Underlying Network ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Superior Approach

Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$, prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$. It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$, uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

scholarly journals PEMODELAN PENYEBARAN KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA DENPASAR DENGAN METODE SPATIAL AUTOREGRESSIVE (SAR)

2017 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
NI MADE SURYA JAYANTI ◽  
I WAYAN SUMARJAYA ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Model ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Hemorrhagic Fever ◽  
Autoregressive Process ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Spatial Autoregressive

One of spatial regression model is Spatial Autoregressive (SAR), which assumes that the autoregressive process only on the dependent variable only by considering the spatial effects. There are two aspects of spatial effects, that is spatial dependence and spatial heterogeneity. One of the problems which considers spatial effect is the spread of Dengue Hemorrhagic Fever (DHF). Denpasar City is an endemic DHF disease because there have been DHF cases in three consecutive years or more. The purpose of this research is to estimate the spread of DHF in  Denpasar City along with the factors that affect it. The results show that the factors that influence the spread of DHF are neighborhood, area and the role of Jumantik at the every village in Denpasar City.

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