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010 a| 2026001762;

020 a| 9781041087694q| hbk;

020 a| 9781041087441q| pbk;

020 z| 9781003646822q| ebk;

035 a| (WaSeSS)ssj0003486000;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| QH323.5b| .R87 2026;

100 1 a| Rustand, Denis0| http://id.loc.gov/authorities/names/n20265106991| http://id.loc.gov/rwo/agents/n2026510699.?| UNAUTHORIZED;

245 10 a| Bayesian survival, longitudinal, and joint models with INLAh| [electronic resource] /c| Denis Rustand, Janet van Niekerk, Elias T. Krainski, and Håvard Rue.;

250 a| First edition.;

260 a| Boca Raton :b| CRC Press,c| 2026.;

263 a| 2605;

300 a| pages cm;

334 a| single unitb| mono0| http://id.loc.gov/vocabulary/issuance/mono;

353 a| bibliographyb| bibliography0| http://id.loc.gov/vocabulary/msupplcont/bibliography;

353 a| indexb| index0| http://id.loc.gov/vocabulary/msupplcont/index;

504 a| Includes bibliographical references and index.;

505 0 a| The integrated nested Laplace approximations methodology -- Survival analysis -- Longitudinal data analysis -- Joint modeling of longitudinal and survival data -- Spatial models.;

506 a| Available only to authorized users.;

520 a| "This book provides a comprehensive and practical guide to fitting complex Bayesian survival, longitudinal and joint models using the Integrated Nested Laplace Approximations (INLA) methodology, a powerful and computationally efficient alternative to traditional MCMC methods. Aimed at graduate students, researchers, and applied statisticians in biostatistics, epidemiology, and public health, this book addresses the critical challenge of analyzing high-dimensional and correlated data. It demonstrates how to move beyond the computational limitations of conventional methods, enabling the analysis of sophisticated models that were previously out of reach"-- Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Survival analysis (Biometry)x| Methodology=| ^A325761;

650 0 a| Regression analysisx| Mathematical models0| http://id.loc.gov/authorities/subjects/sh2009006876=| ^A1194733;

650 0 a| Bayesian statistical decision theory0| http://id.loc.gov/authorities/subjects/sh85012506=| ^A20878;

650 0 a| Laplace transformation0| http://id.loc.gov/authorities/subjects/sh85074666=| ^A46739;

650 0 a| Longitudinal method0| http://id.loc.gov/authorities/subjects/sh85078296=| ^A3660;

650 0 a| Spatial analysis (Statistics)0| http://id.loc.gov/authorities/subjects/sh85126347=| ^A42876;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Van Niekerk, Janet0| http://id.loc.gov/authorities/names/n20265107001| http://id.loc.gov/rwo/agents/n2026510700.?| UNAUTHORIZED;

700 1 a| Rue, Håvard0| http://id.loc.gov/authorities/names/n20040170841| http://id.loc.gov/rwo/agents/n2004017084.=| ^A594409;

758 1| http://id.loc.gov/resources/instances/in00024500141;

758 4| http://id.loc.gov/ontologies/bibframe/instanceOf1| http://id.loc.gov/resources/works/in00024500141;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=4371596&scope=site&custid=s5822723&authtype=ip,shib;

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Bayesian survival, longitudinal, and joint models with INLA / Denis Rustand, Janet van Niekerk, Elias T. Krainski, and Håvard Rue.

Author/creator	Rustand, Denis
Other author	Van Niekerk, Janet
Other author	Krainski, E. T. (Elias T.
Other author	Rue, Håvard
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton : CRC Press, 2026.
Description	pages cm
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Survival analysis (Biometry)--Methodology. Regression analysis--Mathematical models. Bayesian statistical decision theory Laplace transformation Longitudinal method Spatial analysis (Statistics)

Contents	The integrated nested Laplace approximations methodology -- Survival analysis -- Longitudinal data analysis -- Joint modeling of longitudinal and survival data -- Spatial models.
Abstract	"This book provides a comprehensive and practical guide to fitting complex Bayesian survival, longitudinal and joint models using the Integrated Nested Laplace Approximations (INLA) methodology, a powerful and computationally efficient alternative to traditional MCMC methods. Aimed at graduate students, researchers, and applied statisticians in biostatistics, epidemiology, and public health, this book addresses the critical challenge of analyzing high-dimensional and correlated data. It demonstrates how to move beyond the computational limitations of conventional methods, enabling the analysis of sophisticated models that were previously out of reach"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2026001762
ISBN	9781041087694 hbk
ISBN	9781041087441 pbk
ISBN	ebk

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Bayesian survival, longitudinal, and joint models with INLA / Denis Rustand, Janet van Niekerk, Elias T. Krainski, and Håvard Rue.

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