Isbn: 9781482246414 - disease mapping: from foundations to multidimensional modeling (13 risultati)

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  • Lingua: Inglese

    Editore: Chapman and Hall/CRC, 2019

    1482246414 / 9781482246414

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  • Lingua: Inglese

    Editore: Chapman and Hall/CRC, 2019

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  • Lingua: Inglese

    Editore: Taylor & Francis Inc, Oakville, 2019

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    Hardcover. Condizione: new. Hardcover. Disease 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. Guides the reader from the foundations of disease mapping to the most advanced topic in this field - multidimensional modeling. Multidimensional framework makes possible the joint modeling of various risks patterns corresponding to combinations of several factors, such as age group, time period, disease, or sex. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Lingua: Inglese

    Editore: Chapman and Hall/CRC, 2019

    1482246414 / 9781482246414

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    Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK

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  • Lingua: Inglese

    Editore: Taylor & Francis Group, 2019

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  • Lingua: Inglese

    Editore: Taylor & Francis Group, 2019

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  • Lingua: Inglese

    Editore: Chapman and Hall/CRC, 2019

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  • Lingua: Inglese

    Editore: Chapman & Hall, 2019

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    Hardcover. Condizione: Brand New. 432 pages. 9.50x6.25x1.25 inches. In Stock.

  • Lingua: Inglese

    Editore: Apple Academic Press Inc., 2019

    1482246414 / 9781482246414

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    Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

  • Lingua: Inglese

    Editore: CRC Press, 2019

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  • Lingua: Inglese

    Editore: Taylor & Francis Group, 2019

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  • Lingua: Inglese

    Editore: Taylor & Francis Inc Jul 2019, 2019

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    Buch. Condizione: Neu. Neuware - 'A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.'.

  • Lingua: Inglese

    Editore: Taylor & Francis Inc, Oakville, 2019

    1482246414 / 9781482246414

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    Hardcover. Condizione: new. Hardcover. Disease 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. Guides the reader from the foundations of disease mapping to the most advanced topic in this field - multidimensional modeling. Multidimensional framework makes possible the joint modeling of various risks patterns corresponding to combinations of several factors, such as age group, time period, disease, or sex. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.