Wen-Xiu Zhang
Biographic Data
| ID | 1520118 |
|---|---|
| NAME | Wen-Xiu Zhang |
| GIVEN NAMES | Wen-Xiu |
| FAMILY NAME | Zhang |
| SIGNATURE | ZHANG W |
| AFFILIATIONS | Xi'an Jiaotong University |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 47 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2000 |
| LATEST PUBLICATION YEAR | 2006 |
| H-INDEX | 3 |
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…
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…
Econometrics (4 works) · Mathematics (4 works) · Spatial and Panel Data Analysis (4 works) · Statistics (4 works) · Computer Science (3 works) · Regional Economics and Spatial Analysis (3 works) · Regression analysis (3 works) · Spatial analysis (3 works) · Statistical hypothesis testing (3 works) · Economic and Environmental Valuation (2 works)