LEADER 01021nam a2200277 i 4500 001 991000638459707536 005 20020503193121.0 008 940510s1989 us ||| | eng 020 $a0824780531 035 $ab10107101-39ule_inst 035 $aLE02519589$9ExL 040 $aFac. Economia$bita 082 0 $a519.536 100 1 $aBorowiak, Dale S.$0102810 245 10$aModel discrimination for nonlinear regression models /$cDale S. Borowiak 260 $aNew York ; Basel :$bDekker,$cc1989 300 $axi, 177 p. ;$c24 cm 490 0 $aStatistics, textbooks and monographs ;$v101 650 4$aAnalisi della regressione 650 4$aStatistica matematica 907 $a.b10107101$b17-02-17$c27-06-02 912 $a991000638459707536 945 $aLE025 ECO 519.5 BOR01.01$g1$i2025000017689$lle025$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10125383$z27-06-02 996 $aModel discrimination for nonlinear regression models$9197679 997 $aUNISALENTO 998 $ale025$b01-01-94$cm$da $e-$feng$gus $h0$i1 LEADER 04950nam 22007814a 450 001 9911020330003321 005 20200520144314.0 010 $a9786610270392 010 $a9781280270390 010 $a128027039X 010 $a9780470341643 010 $a0470341645 010 $a9780470856055 010 $a047085605X 010 $a9780470856062 010 $a0470856068 035 $a(CKB)111087027100668 035 $a(EBL)164843 035 $a(OCoLC)475873648 035 $a(SSID)ssj0000139885 035 $a(PQKBManifestationID)11151259 035 $a(PQKBTitleCode)TC0000139885 035 $a(PQKBWorkID)10028525 035 $a(PQKB)11769699 035 $a(MiAaPQ)EBC164843 035 $a(Perlego)2758241 035 $a(EXLCZ)99111087027100668 100 $a20030604d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDisease mapping with WinBUGS and MLwiN /$fAndrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cJ. Wiley$dc2003 215 $a1 online resource (293 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 08$a9780470856048 311 08$a0470856041 320 $aIncludes bibliographical references (p. 267-273) and index. 327 $aDisease 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 327 $a3.1 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 327 $a6.2 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 327 $a9.2 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 330 $aDisease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.Provides an introduction to Bayesian and multilevel modelling in disease m 410 0$aStatistics in practice. 606 $aMedical mapping 606 $aMedical geography$xMaps$xData processing 606 $aEpidemiology$xStatistical methods 606 $aEpidemiology$xData processing 606 $aPublic health surveillance 615 0$aMedical mapping. 615 0$aMedical geography$xMaps$xData processing. 615 0$aEpidemiology$xStatistical methods. 615 0$aEpidemiology$xData processing. 615 0$aPublic health surveillance. 676 $a615.4/2/0727 700 $aLawson$b Andrew$g(Andrew B.)$0149118 701 $aBrowne$b William J$g(William John),$f1972-$01838619 701 $aVidal Rodeiro$b Carmen L$01838620 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020330003321 996 $aDisease mapping with WinBUGS and MLwiN$94417645 997 $aUNINA