Dalla geografia nozionistica alla geografia scientifica applicata : la scuola verso i problemi del territorio e della società / a cura di R. Bernardi ; [scritti di R. Bernardi et al.]

• Record Nr.: UNINA990007462530403321
• Pubbl/distr/stampa: Verona : Fac. di Magistero; Bologna : Patron, 1990
• Descrizione fisica: 119 p. ; 24 cm
• Collana: Pubblicazioni dell'Istituto di geografia ; 2
• Locazione: ILFGE
• Collocazione: A-G 0029
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Disease mapping with WinBUGS and MLwiN [[electronic resource] /] / Andrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro

• Record Nr.: UNINA9910143214303321
• Autore: Lawson Andrew (Andrew B.)
• Pubbl/distr/stampa: Chichester, West Sussex, England ; ; Hoboken, NJ, : J. Wiley, c2003
• ISBN: 1-280-27039-X; 9786610270392; 0-470-34164-5; 0-470-85605-X; 0-470-85606-8
• Descrizione fisica: 1 online resource (293 p.)
• Collana: Statistics in practice
• Altri autori (Persone): BrowneWilliam J <1972-> (William John); Vidal RodeiroCarmen L
• Disciplina: 614; 614.4202855369; 615.4/2/0727
• Soggetti: Medical mapping; Medical geography - Maps - Data processing; Epidemiology - Statistical methods; Epidemiology - Data processing; Public health surveillance; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 267-273) and index.
• Nota di contenuto: Disease Mapping with WinBUGS and MLwiN; Contents; Preface; Notation; 0.1 Standard notation for multilevel modelling; 0.2 Spatial multiple-membership models and the MMMC notation; 0.3 Standard notation for WinBUGS models; 1 Disease mapping basics; 1.1 Disease mapping and map reconstruction; 1.2 Disease map restoration; 2 Bayesian hierarchical modelling; 2.1 Likelihood and posterior distributions; 2.2 Hierarchical models; 2.3 Posterior inference; 2.4 Markov chain Monte Carlo methods; 2.5 Metropolis and Metropolis-Hastings algorithms; 2.6 Residuals and goodness of fit; 3 Multilevel modelling; 3.1 Continuous response models3.2 Estimation procedures for multilevel models; 3.3 Poisson response models; 3.4 Incorporating spatial information; 3.5 Discussion; 4 WinBUGS basics; 4.1 About WinBUGS; 4.2 Start using WinBUGS; 4.3 Specification of the model; 4.4 Model fitting; 4.5 Scripts; 4.6 Checking convergence; 4.7 Spatial modelling: GeoBUGS; 4.8 Conclusions; 5 MLwiN basics; 5.1 About MLwiN; 5.2 Getting started; 5.3 Fitting statistical models; 5.4 MCMC estimation in MLwiN; 5.5 Spatial modelling; 5.6 Conclusions; 6 Relative risk estimation; 6.1 Relative risk estimation using WinBUGS; 6.2 Spatial prediction6.3 An analysis of the Ohio dataset using MLwiN; 7 Focused clustering: the analysis of putative health hazards; 7.1 Introduction; 7.2 Study design; 7.3 Problems of inference; 7.4 Modelling the hazard exposure risk; 7.5 Models for count data; 7.6 Bayesian models; 7.7 Focused clustering in WinBUGS; 7.8 Focused clustering in MLwiN; 8 Ecological analysis; 8.1 Introduction; 8.2 Statistical models; 8.3 WinBUGS analyses of ecological datasets; 8.4 MLwiN analyses of ecological datasets; 9 Spatially-correlated survival analysis; 9.1 Survival analysis in WinBUGS; 9.2 Survival analysis in MLwiN10 Epilogue; Appendix 1: WinBUGS code for focused clustering models; A.1 Falkirk example; A.2 Ohio example; Appendix 2: S-Plus function for conversion to GeoBUGS format; Bibliography; Index
• Sommario/riassunto: Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.Provides an introduction to Bayesian and multilevel modelling in disease m