In many problems in the field of spatial statistics, when modeling the trend functions, predictors or covariates are available and the goal is to build a regression model to describe the relationship between the response and predictors. Generally, in spatial regression models, the trend function is often linear and it is assumed that the response mean is a linear function of predictor values in the same location where the response variable is observed. But, in real applications, the neighboring predictors sometimes provide valuable information about the response variable particulary when the distance between the locations is small. Having considered this subject matter, Heaton and Gelfand [6] suggested using kernel averaged predictors for modeling trend functions in which neighboring predictor information are also used. The models proposed by Heaton an Gelfand seemed to be bound by data normality. So, in many more application problems, spatial response variables follow a skew distribution. Therefore, in this article, skew Gaussian spatial regression model is studied and the performance of the model is presented and evaluated in comparison with Gaussian spatial regression models based on kernel averaged predictors using simulation studies and real examples
| Published in | American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5) |
| DOI | 10.11648/j.ajtas.20150405.17 |
| Page(s) | 368-372 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Spatial Regression, Kernel, Skew Normal
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APA Style
Somayeh Shahraki Dehsoukhteh. (2015). Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors. American Journal of Theoretical and Applied Statistics, 4(5), 368-372. https://doi.org/10.11648/j.ajtas.20150405.17
ACS Style
Somayeh Shahraki Dehsoukhteh. Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors. Am. J. Theor. Appl. Stat. 2015, 4(5), 368-372. doi: 10.11648/j.ajtas.20150405.17
AMA Style
Somayeh Shahraki Dehsoukhteh. Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors. Am J Theor Appl Stat. 2015;4(5):368-372. doi: 10.11648/j.ajtas.20150405.17
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Download@article{10.11648/j.ajtas.20150405.17,
author = {Somayeh Shahraki Dehsoukhteh},
title = {Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {4},
number = {5},
pages = {368-372},
doi = {10.11648/j.ajtas.20150405.17},
url = {https://doi.org/10.11648/j.ajtas.20150405.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.17},
abstract = {In many problems in the field of spatial statistics, when modeling the trend functions, predictors or covariates are available and the goal is to build a regression model to describe the relationship between the response and predictors. Generally, in spatial regression models, the trend function is often linear and it is assumed that the response mean is a linear function of predictor values in the same location where the response variable is observed. But, in real applications, the neighboring predictors sometimes provide valuable information about the response variable particulary when the distance between the locations is small. Having considered this subject matter, Heaton and Gelfand [6] suggested using kernel averaged predictors for modeling trend functions in which neighboring predictor information are also used. The models proposed by Heaton an Gelfand seemed to be bound by data normality. So, in many more application problems, spatial response variables follow a skew distribution. Therefore, in this article, skew Gaussian spatial regression model is studied and the performance of the model is presented and evaluated in comparison with Gaussian spatial regression models based on kernel averaged predictors using simulation studies and real examples},
year = {2015}
}
TY - JOUR T1 - Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors AU - Somayeh Shahraki Dehsoukhteh Y1 - 2015/08/19 PY - 2015 N1 - https://doi.org/10.11648/j.ajtas.20150405.17 DO - 10.11648/j.ajtas.20150405.17 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 368 EP - 372 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20150405.17 AB - In many problems in the field of spatial statistics, when modeling the trend functions, predictors or covariates are available and the goal is to build a regression model to describe the relationship between the response and predictors. Generally, in spatial regression models, the trend function is often linear and it is assumed that the response mean is a linear function of predictor values in the same location where the response variable is observed. But, in real applications, the neighboring predictors sometimes provide valuable information about the response variable particulary when the distance between the locations is small. Having considered this subject matter, Heaton and Gelfand [6] suggested using kernel averaged predictors for modeling trend functions in which neighboring predictor information are also used. The models proposed by Heaton an Gelfand seemed to be bound by data normality. So, in many more application problems, spatial response variables follow a skew distribution. Therefore, in this article, skew Gaussian spatial regression model is studied and the performance of the model is presented and evaluated in comparison with Gaussian spatial regression models based on kernel averaged predictors using simulation studies and real examples VL - 4 IS - 5 ER -
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