000			03861cam a2200529Ii 4500
001			9781315118741
003			FlBoTFG
005			20220509193139.0
006			m o d
007			cr cnu|||unuuu
008			190705s2019 flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781351645027 _q(electronic bk.)
020			_a1351645021 _q(electronic bk.)
020			_a9781315118741 _q(electronic bk.)
020			_a1315118742 _q(electronic bk.)
020			_a9781351635530 _q(electronic bk. : Mobipocket)
020			_a1351635530 _q(electronic bk. : Mobipocket)
020			_a9781482246421 _q(electronic bk. : PDF)
020			_a1482246422 _q(electronic bk. : PDF)
020			_z9781482246414
035			_a(OCoLC)1107493672
035			_a(OCoLC-P)1107493672
050		4	_aRA792.5
072		7	_aMAT _x029000 _2bisacsh
072		7	_aMED _x028000 _2bisacsh
072		7	_aREF _x000000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a614.42 _223
100	1		_aMartínez-Beneito, Miguel A., _eauthor.
245	1	0	_aDisease mapping : _bfrom foundations to multidimensional modeling / _cMiguel A. Martinez-Beneito, Paloma Botella-Rocamora.
264		1	_aBoca Raton, FL : _bCRC Press, Taylor & Francis Group, _c[2019]
264		4	_c©2019
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aDisease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aMedical mapping.
650		0	_aEpidemiology _xStatistical methods.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		7	_aMEDICAL / Epidemiology _2bisacsh
650		7	_aREFERENCE / General _2bisacsh
700	1		_aBotella-Rocamora, Paloma, _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315118741
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c130855 _d130855