© 2020

Statistical Modeling for Biological Systems

In Memory of Andrei Yakovlev

  • Anthony Almudevar
  • David Oakes
  • Jack Hall

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Research Articles

    1. Front Matter
      Pages 1-1
    2. Linlin Chen, Lev Klebanov, Anthony Almudevar, Christoph Proschel, Andrei Yakovlev
      Pages 47-61
    3. Suresh Moolgavkar, Georg Luebeck
      Pages 117-136
    4. W. J. Hall, Jon A. Wellner
      Pages 169-189
    5. Alex Tsodikov, Lyrica Xiaohong Liu, Carol Tseng
      Pages 191-209
  3. Short Communications

    1. Front Matter
      Pages 279-279
    2. Jeffrey Miecznikowski, Dan Wang, Xing Ren, Jianmin Wang, Song Liu
      Pages 289-296

About this book


This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. 
Part A consists of original research articles, which can be roughly grouped into four thematic areas, (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. 
Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. 
The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former Presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.


Statistical Modeling Biological Systems Biostatistics Computational Biology Andrei Yakovlev Branching Processes Cancer Cell Kinetics Machine Learning Multiple Comparisons Survivial Analysis

Editors and affiliations

  • Anthony Almudevar
    • 1
  • David Oakes
    • 2
  • Jack Hall
    • 3
  1. 1.Department of Biostatistics and Computational BiologyUniversity of Rochester Medical CenterRochesterUSA
  2. 2.Department of Biostatistics and Computational BiologyUniversity of Rochester Medical CenterRochesterUSA
  3. 3.RochesterUSA

About the editors

​Anthony Almudevar is an Associate Professor of Biostatistics and Computational Biology at the University of Rochester. He received his Ph.D from the Department of Statistics at the University of Toronto. His research interests include genetics, bioinformatics, optimization and control theory, particularly in the area of Markov Decision Processes. 

David Oakes is a Professor of Biostatistics and Statistics at the University of Rochester. He received his Ph.D from London University. His major research interests are in the area of survival analysis, especially models for the effect of explanatory variables on survival and for multivariate survival data. More recent work has largely focused on the models for bivariate survival models generated by unobserved random effects, often called frailties.

W. Jackson “Jack” Hall, Ph.D., was Emeritus Professor of Statistics and Professor in the Department of Biostatistics and Computational Biology at the University of Rochester. He was an international authority on statistical designs for modern clinical trials. Throughout a distinguished career that spanned more than 40 years in Rochester, Dr. Hall made numerous contributions to the field of mathematical statistics.

Bibliographic information

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