Nonparametric Estimation For Spatial Data By Weighted Lad Regression
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Author | : Douglas D. Walker |
Publisher | : |
Total Pages | : 366 |
Release | : 1994 |
Genre | : Geology |
ISBN | : |
Download Nonparametric Estimation for Spatial Data by Weighted Lad Regression Book in PDF, Epub and Kindle
Author | : Daniel P. McMillen |
Publisher | : Springer Science & Business Media |
Total Pages | : 69 |
Release | : 2012-08-01 |
Genre | : Business & Economics |
ISBN | : 3642318150 |
Download Quantile Regression for Spatial Data Book in PDF, Epub and Kindle
Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail.
Author | : A. Stewart Fotheringham |
Publisher | : John Wiley & Sons |
Total Pages | : 282 |
Release | : 2003-02-21 |
Genre | : Science |
ISBN | : 0470855258 |
Download Geographically Weighted Regression Book in PDF, Epub and Kindle
Geographical Weighted Regression (GWR) is a new local modelling technique for analysing spatial analysis. This technique allows local as opposed to global models of relationships to be measured and mapped. This is the first and only book on this technique, offering comprehensive coverage on this new 'hot' topic in spatial analysis. * Provides step-by-step examples of how to use the GWR model using data sets and examples on issues such as house price determinants, educational attainment levels and school performance statistics * Contains a broad discussion of and basic concepts on GWR through to ideas on statistical inference for GWR models * uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details).
Author | : Nicola Salvati |
Publisher | : |
Total Pages | : 11 |
Release | : 2005 |
Genre | : |
ISBN | : |
Download M-quantile Geographically Weighted Regression for Nonparametric Small Area Estimation Book in PDF, Epub and Kindle
Author | : |
Publisher | : |
Total Pages | : 180 |
Release | : 1978 |
Genre | : |
ISBN | : |
Download Nonparametric Estimation of Weights in Least-squares Regression Analysis Book in PDF, Epub and Kindle
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2006 |
Genre | : Parameter estimation |
ISBN | : |
Download Nonparametric Spectrum Estimation for Spatial Data Book in PDF, Epub and Kindle
Author | : Daniel Griffith |
Publisher | : Academic Press |
Total Pages | : 286 |
Release | : 2019-09-14 |
Genre | : Business & Economics |
ISBN | : 0128156929 |
Download Spatial Regression Analysis Using Eigenvector Spatial Filtering Book in PDF, Epub and Kindle
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models Includes computer code and template datasets for further modeling Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Author | : Michael Don Ward |
Publisher | : SAGE |
Total Pages | : 113 |
Release | : 2008-02-29 |
Genre | : Mathematics |
ISBN | : 1412954150 |
Download Spatial Regression Models Book in PDF, Epub and Kindle
Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.
Author | : Johannes Theodor Nikolaus Krebs |
Publisher | : |
Total Pages | : |
Release | : 2017 |
Genre | : |
ISBN | : 9783839611869 |
Download Sieve Estimators for Spatial Data Book in PDF, Epub and Kindle
Author | : Guangqing Chi |
Publisher | : SAGE Publications |
Total Pages | : 229 |
Release | : 2019-03-06 |
Genre | : Social Science |
ISBN | : 1544302053 |
Download Spatial Regression Models for the Social Sciences Book in PDF, Epub and Kindle
Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.