Posted by **DZ123** at May 23, 2019

English | 2013 | ISBN: 1118521218 | PDF | pages: 343 | 2.3 mb

Posted by **interes** at April 11, 2019

English | 2012 | ISBN: 1119941822 | 598 pages | PDF | 11,8 MB

Posted by **step778** at Dec. 10, 2018

2000 | pages: 301 | ISBN: 1584881747 | PDF | 8,4 mb

Posted by **AvaxGenius** at July 11, 2018

English | PDF(Repost),EPUB | 2014 | 250 Pages | ISBN : 3319045784 | 6.44 MB

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, © Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.

Posted by **AvaxGenius** at Aug. 1, 2018

English | PDF(Repost),EPUB | 2014 | 250 Pages | ISBN : 3319045784 | 6.44 MB

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, © Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.

Posted by **AvaxGenius** at Aug. 13, 2018

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, © Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples.

Posted by **arundhati** at Sept. 16, 2018

2007 | ISBN: 0470018755 | 596 pages | PDF | 3 MB

Posted by **ChrisRedfield** at Oct. 11, 2018

Published: 2012-03-26 | ISBN: 3527324348 | PDF | 456 pages | 5.43 MB

Posted by **fdts** at Oct. 16, 2016

by Carles M. Cuadras, Josep Fortiana, José A. Rodríguez-Lallena

English | 2002 (2013) | ISBN: 9048161363 | 244 pages | PDF | 7.57 MB

Posted by **AvaxGenius** at Jan. 3, 2018

English | PDF,EPUB | 2017 | 288 Pages | ISBN : 9811065551 | 9.52 MB

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration.