A New Method of Hypothesis Test for Truncated Spline Nonparametric Regression Influenced by Spatial Heterogeneity and Application

Sifriyani, Sifriyani; Budiantara, I Nyoman; Kartiko, Sri Haryatmi; Gunardi, .

doi:10.1155/2018/9769150

Cited by 25 publications

(15 citation statements)

References 9 publications

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“…The analysis step was carried out in 5 stages. They are model estimation [15], selection of optimal knot points (Sifriyani, Haryatmi, I.N Budiantara, & Gunardi, 2017), testing the suitability of the model hypothesis (Sifriyani, I.N. Budiantara, S.H.…”

Section: Methodsmentioning

confidence: 99%

“…The method used in this research is Nonparametric Spatial Regression with Geographic Weighting. The step analysis was carried out using five stages: estimation of the model (Sifriyani, S. H. Kartiko, I. N. Budiantara & Gunardi, 2018), selection of optimal knot points (Sifriyani, Haryatmi, I.N Budiantara, & Gunardi, 2017), testing the suitability hypothesis of the model (Sifriyani, I.N. Budiantara, S.H.…”

Section: Nonparametric Spatial Regression Methods With Geographic Weigmentioning

confidence: 99%

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Identification of Factors Affecting Food Productivity Improvement in Kalimantan Using Nonparametric Spatial Regression Method

Sifriyani¹,

Suyitno²,

Rizki³

2019

MAS

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Problems of Food Productivity in Kalimantan is experiencing instability. Every year, various problems and inhibiting factors that cause the independence of food production in Kalimantan are suffering a setback. The food problems in Kalimantan requires a solution, therefore this study aims to analyze the factors that influence the increase of productivity and production of food crops in Kalimantan using Spatial Statistics Analysis. The method used is Nonparametric Spatial Regression with Geographic Weighting. Sources of research data used are secondary data and primary data obtained from the Ministry of Agriculture and the Central Statistics Agency. The total area used is 56 regencies/cities in Kalimantan. The results show that there are 13 factors affect food productivity in Kalimantan. These factors are the number of agricultural business households, the number of workers in agriculture, the amount of rice production, rice field area, rice field harvest area, irrigation network area, area of each region, total area based on altitude class, area based on slope/slope class, economic growth rate, Gross Regional Domestic Product, Regional Development Index and Population Density. The best model is obtained using the geographical weighting of the Gaussian kernel function with the cross-validation value 5,65. The criteria for the goodness of the model with the number of knots 3 and order m = 1 have R2 value of 97,19% and the value of AIC is 2,43.

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Section: Methodsmentioning

confidence: 99%

Section: Nonparametric Spatial Regression Methods With Geographic Weigmentioning

confidence: 99%

Identification of Factors Affecting Food Productivity Improvement in Kalimantan Using Nonparametric Spatial Regression Method

Sifriyani¹,

Suyitno²,

Rizki³

2019

MAS

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“…After obtaining the result of hypothesis testing which stated that the Multivariable Nonparametric Geographically Weighted Regression Use Truncated Spline Approach is not the same as the nonparametric truncated spline regression model (Sifriyani, 2018b). Further research is to perform simultaneous test of parameters for Multivariable Nonparametric Geographically Weighted Regression Use Truncated Spline Approach with hypothetical form (2).…”

Section: Parameter Estimation Under Hypothesis H 0 and H 1 For Multivmentioning

confidence: 99%

Simultaneous Hypothesis Testing of Multivariable Nonparametric Spline Regression in the GWR Model

Sifriyani¹

2019

IJSP

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In this research, studied multivariable nonparametric geographically weighted regression use truncated spline approach. The model is an expansion of nonparametric truncated spline regression that takes into account geographical or spatial factors. The purpose of this study was to find statistics test and distribution for the simultaneous hypothesis test. This study obtains the statistic test used the maximum likelihood ratio test (MLRT) method. Results of the research obtained statistics test based on the ratio between the maximum of the likelihood function under the set of H_0  and the maximum of the set likelihood function below the population with each have a spatial factor. Distribution of statistical tests has been proven to have a distribution of F. The modeling application used the percentage of the death of Dengue Hemorrhagic Fever (DHF) in 38 districts/cities in East Java Province. The modeling resulted in the determination coefficient of 80.7% and SSE value that is 0.0043.

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“…The MLE method and the expectationmaximization algorithm were applied to estimate the GWMtR model parameters. In [8], a new method to determine model conformity between the multivariate nonparametric truncated spline GWR model and the multivariate nonparametric truncated spline (global regression) was employed.…”

Section: Introductionmentioning

confidence: 99%

Geographically Weighted Multivariate Logistic Regression Model and Its Application

Fathurahman

Purhadi

Sutikno

et al. 2020

Abstract and Applied Analysis

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This study investigates the geographically weighted multivariate logistic regression (GWMLR) model, parameter estimation, and hypothesis testing procedures. The GWMLR model is an extension to the multivariate logistic regression (MLR) model, which has dependent variables that follow a multinomial distribution along with parameters associated with the spatial weighting at each location in the study area. The parameter estimation was done using the maximum likelihood estimation and Newton-Raphson methods, and the maximum likelihood ratio test was used for hypothesis testing of the parameters. The performance of the GWMLR model was evaluated using a real dataset and it was found to perform better than the MLR model.

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A New Method of Hypothesis Test for Truncated Spline Nonparametric Regression Influenced by Spatial Heterogeneity and Application

Cited by 25 publications

References 9 publications

Identification of Factors Affecting Food Productivity Improvement in Kalimantan Using Nonparametric Spatial Regression Method

Identification of Factors Affecting Food Productivity Improvement in Kalimantan Using Nonparametric Spatial Regression Method

Simultaneous Hypothesis Testing of Multivariable Nonparametric Spline Regression in the GWR Model

Geographically Weighted Multivariate Logistic Regression Model and Its Application

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