Statistical Modelling in Biostatistics and Bioinformatics

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   • Gilbert MacKenzie, Defen Peng

   Pages 1-6

3. Survival Modelling

   1. Front Matter

      Pages 7-7

      PDF

   2. Multivariate Interval-Censored Survival Data: Parametric, Semi-parametric and Non-parametric Models

      • Philip Hougaard

      Pages 9-21

   3. Multivariate Survival Models Based on the GTDL

      • Gilbert MacKenzie, Il Do Ha

      Pages 23-34

   4. Frailty Models with Structural Dispersion

      • Joseph Lynch, Gilbert MacKenzie

      Pages 35-44

   5. Random Effects Ordinal Time Models for Grouped Toxicological Data from a Biological Control Assay

      • Marie-José Martinez, John P. Hinde

      Pages 45-58

4. Longitudinal Modelling and Time Series

   1. Front Matter

      Pages 59-59

      PDF

   2. Modelling Seasonality and Structural Breaks: Visitors to NZ and 9/11

      • John Haywood, John Randal

      Pages 61-80

   3. Forecasting the Risk of Insolvency Among Customers of an Automotive Financial Service

      • Rita Allais, Marco Bosco

      Pages 81-92

   4. On Joint Modelling of Constrained Mean and Covariance Structures in Longitudinal Data

      • Jing Xu, Gilbert MacKenzie

      Pages 93-107

5. Statistical Model Development

   1. Front Matter

      Pages 109-109

      PDF

   2. Hierarchical Generalized Nonlinear Models

      • Roger W. Payne

      Pages 111-124

   3. Comparing Robust Regression Estimators to Detect Data Clusters: A Case Study

      • Alessandra Durio, Ennio Isaia

      Pages 125-138

   4. Finite Mixture Model Clustering of SNP Data

      • Norma Bargary, J. Hinde, A. Augusto F. Garcia

      Pages 139-157

   5. Discrepancy and Choice of Reference Subclass in Categorical Regression Models

      • Defen Peng, Gilbert MacKenzie

      Pages 159-184

6. Applied Statistical Modelling

   1. Front Matter

      Pages 185-185

      PDF

   2. Statistical Methods for Detecting Selective Sweeps

      • David Ramsey

      Pages 187-211

   3. A Mixture Model and Bootstrap Analysis to Assess Reproductive Allocation in Plants

      • Caroline Brophy, D. Gibson, P. W. Wayne, J. Connolly

      Pages 213-219

   4. On Model Selection Algorithms in Multi-dimensional Contingency Tables

      • Susana Conde, Gilbert MacKenzie

      Pages 221-242

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 andGalway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.

• covariance modelling
• hierarchical generalised non linear models
• multivariate survival analysis
• statistical genetics
• statistical modelling

From the book reviews:

“The book under review consists of four parts covering survival analysis (Chapters 1–4), longitudinal modeling and time series (Chapters 5–7), statistical model development (Chapters 8–11) and applied statistical modeling (Chapters 12–14). … the variety of topics make it a must-have for a computational biology/bioinformatics lab.” (Irina Ioana Mohorianu, zbMATH, Vol. 1295, 2014)

• Centre of Biostatistics, University of Limerick, Limerick, Ireland

  Gilbert MacKenzie, Defen Peng

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.

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