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Quantile Regression for Spatial Data:

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Bibliographic Details
Main Author:	McMillen, Daniel P. 1958- (Author)
Format:	Electronic eBook
Language:	English
Published:	Berlin, Heidelberg Springer 2013
Series:	SpringerBriefs in Regional Science
Subjects:	Raumdaten Quantil Regressionsanalyse
Online Access:	BTU01 FHA01 FHM01 FHN01 FHO01 FHR01 FKE01 FNU01 FWS01 FWS02 HWR01 UBG01 UBM01 UBT01 UBY01 UPA01 Volltext Inhaltsverzeichnis Abstract
Physical Description:	1 Online-Ressource
ISBN:	9783642318146 9783642318153
DOI:	10.1007/978-3-642-31815-3

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Record in the Search Index

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adam_text	QUANTILE REGRESSION FOR SPATIAL DATA / MCMILLEN, DANIEL P. : 2013 TABLE OF CONTENTS / INHALTSVERZEICHNIS 1QUANTILE REGRESSION: AN OVERVIEW. 2 LINEAR AND NONPARAMETRIC QUANTILE REGRESSION 3 A QUANTILE REGRESSION ANALYSIS OF ASSESSMENT REGRESSIVITY.-4 QUANTILE VERSION OF THE SPATIAL AR MODEL 5 . CONDITIONALLY PARAMETRIC QUANTILE REGRESSION.-6 GUIDE TO FURTHER READING REFERENCES DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT. QUANTILE REGRESSION FOR SPATIAL DATA / MCMILLEN, DANIEL P. : 2013 ABSTRACT / INHALTSTEXT 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 DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
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spellingShingle	McMillen, Daniel P. 1958- Quantile Regression for Spatial Data Raumdaten (DE-588)4206012-6 gnd Quantil (DE-588)4224812-7 gnd Regressionsanalyse (DE-588)4129903-6 gnd
subject_GND	(DE-588)4206012-6 (DE-588)4224812-7 (DE-588)4129903-6
title	Quantile Regression for Spatial Data
title_auth	Quantile Regression for Spatial Data
title_exact_search	Quantile Regression for Spatial Data
title_full	Quantile Regression for Spatial Data Daniel P. McMillen
title_fullStr	Quantile Regression for Spatial Data Daniel P. McMillen
title_full_unstemmed	Quantile Regression for Spatial Data Daniel P. McMillen
title_short	Quantile Regression for Spatial Data
title_sort	quantile regression for spatial data
topic	Raumdaten (DE-588)4206012-6 gnd Quantil (DE-588)4224812-7 gnd Regressionsanalyse (DE-588)4129903-6 gnd
topic_facet	Raumdaten Quantil Regressionsanalyse
url	https://doi.org/10.1007/978-3-642-31815-3 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025375002&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025375002&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT mcmillendanielp quantileregressionforspatialdata

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