Chang-Lin Mei
Biographic Data
| ID | 1340724 |
|---|---|
| NAME | Chang-Lin Mei |
| GIVEN NAMES | Chang-Lin |
| FAMILY NAME | Mei |
| SIGNATURE | MEI C |
| AFFILIATIONS | Xi'an Jiaotong University |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 54 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2000 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 4 |
Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models
Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, only temporally varying, and only spatially varying coefficients might also be possible depending on the underlying effects of the explanatory variables on the response variable. Therefore, the develop…
A Unified Bootstrap Test for Local Patterns of Spatiotemporal Association
There has been a growing interest in using local statistics to identify spatial or spatiotemporal association patterns among georeferenced data, in which the null distributions of the statistics play a key role for the confirmatory inferences. In this study we focus on a generic form of local spatiotemporal statistics and propose a unified bootstrap approach to derive the null distribution of the statistic. In particular, the spatiotemporal varia…
Spatially Varying Coefficient Models
In the framework of the geographically weighted regression technique, spatial homoscedasticity of the model error term is a common assumption when a spatially varying coefficient model is calibrated to explore spatial nonstationarity of the regression relationship. In many real-world problems, however, this assumption cannot be guaranteed. In this study we first present a residual-based test method for detecting spatial heteroscedasticity of a sp…
Local Linear Estimation of Spatially Varying Coefficient Models
Geographically weighted regression (GWR), as a useful method for exploring spatial non-stationarity of a regression relationship, has been applied to a variety of areas. In this method a spatially varying coefficient model is locally calibrated and the spatial-variation patterns of the locally estimated regression coefficients are taken as the main evidence of spatial nonstationarity for the underlying data-generating processes. Therefore, the va…
Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model
A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory va…
Statistical Test for Local Patterns of Spatial Association
In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis G statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing this kind of analysis, and a randomized permutation approach and normal approximation are commonly used to derive the p-values of the statistics. To circ…
Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression
Geographically weighted regression (GWR) is a useful technique for exploring spatial nonstationarity by calibrating, for example, a regression model which allows different relationships to exist at different points in space. In this line of research, many spatial data sets have been successfully analyzed and some statistical tests for spatial variation have been developed. However, an important assumption in these studies is that the disturbance …
Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been developed since the inception of the model. In this paper the authors focus mainly on the development of statistical testing methods relating to this model. Some …
Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been developed since the inception of the model. In this paper the authors focus mainly on the development of statistical testing methods relating to this model. Some …
Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression
Geographically weighted regression (GWR) is a useful technique for exploring spatial nonstationarity by calibrating, for example, a regression model which allows different relationships to exist at different points in space. In this line of research, many spatial data sets have been successfully analyzed and some statistical tests for spatial variation have been developed. However, an important assumption in these studies is that the disturbance …
Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model
A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory va…
Local Linear Estimation of Spatially Varying Coefficient Models
Geographically weighted regression (GWR), as a useful method for exploring spatial non-stationarity of a regression relationship, has been applied to a variety of areas. In this method a spatially varying coefficient model is locally calibrated and the spatial-variation patterns of the locally estimated regression coefficients are taken as the main evidence of spatial nonstationarity for the underlying data-generating processes. Therefore, the va…
Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models
Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, only temporally varying, and only spatially varying coefficients might also be possible depending on the underlying effects of the explanatory variables on the response variable. Therefore, the develop…
Statistical Test for Local Patterns of Spatial Association
In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis G statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing this kind of analysis, and a randomized permutation approach and normal approximation are commonly used to derive the p-values of the statistics. To circ…
Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression
Geographically weighted regression (GWR) is a useful technique for exploring spatial nonstationarity by calibrating, for example, a regression model which allows different relationships to exist at different points in space. In this line of research, many spatial data sets have been successfully analyzed and some statistical tests for spatial variation have been developed. However, an important assumption in these studies is that the disturbance …
Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been developed since the inception of the model. In this paper the authors focus mainly on the development of statistical testing methods relating to this model. Some …
Statistical Test for Local Patterns of Spatial Association
In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis G statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing this kind of analysis, and a randomized permutation approach and normal approximation are commonly used to derive the p-values of the statistics. To circ…
Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model
A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory va…
Local Linear Estimation of Spatially Varying Coefficient Models
Geographically weighted regression (GWR), as a useful method for exploring spatial non-stationarity of a regression relationship, has been applied to a variety of areas. In this method a spatially varying coefficient model is locally calibrated and the spatial-variation patterns of the locally estimated regression coefficients are taken as the main evidence of spatial nonstationarity for the underlying data-generating processes. Therefore, the va…
Spatially Varying Coefficient Models
In the framework of the geographically weighted regression technique, spatial homoscedasticity of the model error term is a common assumption when a spatially varying coefficient model is calibrated to explore spatial nonstationarity of the regression relationship. In many real-world problems, however, this assumption cannot be guaranteed. In this study we first present a residual-based test method for detecting spatial heteroscedasticity of a sp…
A Unified Bootstrap Test for Local Patterns of Spatiotemporal Association
There has been a growing interest in using local statistics to identify spatial or spatiotemporal association patterns among georeferenced data, in which the null distributions of the statistics play a key role for the confirmatory inferences. In this study we focus on a generic form of local spatiotemporal statistics and propose a unified bootstrap approach to derive the null distribution of the statistic. In particular, the spatiotemporal varia…
Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models
Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, only temporally varying, and only spatially varying coefficients might also be possible depending on the underlying effects of the explanatory variables on the response variable. Therefore, the develop…
Mathematics (8 works) · Spatial and Panel Data Analysis (8 works) · Statistics (8 works) · Computer Science (6 works) · Econometrics (6 works) · Economic and Environmental Valuation (5 works) · Linear regression (5 works) · Regional Economics and Spatial Analysis (5 works) · Regression analysis (5 works) · Regression (4 works)