Muutke küpsiste eelistusi

E-raamat: Statistical Modelling in Biostatistics and Bioinformatics: Selected Papers

Edited by , Edited by
  • Formaat: PDF+DRM
  • Sari: Contributions to Statistics
  • Ilmumisaeg: 08-May-2014
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319045795
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Sari: Contributions to Statistics
  • Ilmumisaeg: 08-May-2014
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319045795

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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, (c) 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.

Arvustused

From the book reviews:

The book under review consists of four parts covering survival analysis (Chapters 14), longitudinal modeling and time series (Chapters 57), statistical model development (Chapters 811) and applied statistical modeling (Chapters 1214). the variety of topics make it a must-have for a computational biology/bioinformatics lab. (Irina Ioana Mohorianu, zbMATH, Vol. 1295, 2014)

Introduction 1(8)
Gilbert MacKenzie
Defen Peng
Part I Survival Modelling
Multivariate Interval-Censored Survival Data: Parametric, Semi-parametric and Non-parametric Models
9(14)
Philip Hougaard
Multivariate Survival Models Based on the GTDL
23(12)
Gilbert MacKenzie
Il Do Ha
Frailty Models with Structural Dispersion
35(10)
Joseph Lynch
Gilbert MacKenzie
Random Effects Ordinal Time Models for Grouped Toxicological Data from a Biological Control Assay
45(16)
Marie-Jose Martinez
John P. Hinde
Part II Longitudinal Modelling and Time Series
Modelling Seasonality and Structural Breaks: Visitors to NZ and 9/11
61(20)
John Haywood
John Randal
Forecasting the Risk of Insolvency Among Customers of an Automotive Financial Service
81(12)
Rita Allais
Marco Bosco
On Joint Modelling of Constrained Mean and Covariance Structures in Longitudinal Data
93(18)
Jing Xu
Gilbert MacKenzie
Part III Statistical Model Development
Hierarchical Generalized Nonlinear Models
111(14)
Roger W. Payne
Comparing Robust Regression Estimators to Detect Data Clusters: A Case Study
125(14)
Alessandra Durio
Ennio Isaia
Finite Mixture Model Clustering of SNP Data
139(20)
Norma Bargary
J. Hinde
A. Augusto
F. Garcia
Discrepancy and Choice of Reference Subclass in Categorical Regression Models
159(28)
Defen Peng
Gilbert MacKenzie
Part IV Applied Statistical Modelling
Statistical Methods for Detecting Selective Sweeps
187(26)
David Ramsey
A Mixture Model and Bootstrap Analysis to Assess Reproductive Allocation in Plants
213(8)
Caroline Brophy
D. Gibson
P.W. Wayne
J. Connolly
On Model Selection Algorithms in Multi-dimensional Contingency Tables
221(22)
Susana Conde
Gilbert MacKenzie
Obituary: Professor Ennio Isaia 243
Professor Gilbert MacKenzie has a research background in Epidemiology, Biostatistics and Mathematical Statistics. His current research interests include multivariate survival modelling, frailty modelling and covariance modelling. He has published a wide range of research papers and reports and is a past President of the Irish Statistical Association. He holds an adjunct Professorship in Statistics in the University of Limerick and was a visiting Professor in Statistics at ENSAI, France, from 2010 to 2011.

Professor Defen Peng was a visiting Professor in Statistics and senior Research Fellow in the BIO-SI research programme at Limerick from 2009 to 2010. Professor Peng originally worked in the field of Economics at Zhongnan University of Economics and Law, PRC. She has published widely and is currently pursuing several areas of Statistics, such as: survival analysis with frailty, bivariate survival analysis, and the stability of regression models with categoricalcovariates.