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Statistics for the Biological Sciences Books

You are currently browsing 41–50 of 165 new and published books in the subject of Statistics for the Biological Sciences — sorted by publish date from newer books to older books.

For books that are not yet published; please browse forthcoming books.

New and Published Books – Page 5

  1. Age-Period-Cohort Analysis

    New Models, Methods, and Empirical Applications

    By Yang Yang, Kenneth C. Land

    Series: Chapman & Hall/CRC Interdisciplinary Statistics

    Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three...

    Published February 25th 2013 by Chapman and Hall/CRC

  2. Analysis of Mixed Data

    Methods & Applications

    Edited by Alexander R. de Leon, Keumhee Carrière Chough

    A comprehensive source on mixed data analysis, Analysis of Mixed Data: Methods & Applications summarizes the fundamental developments in the field. Case studies are used extensively throughout the book to illustrate interesting applications from economics, medicine and health, marketing, and...

    Published January 16th 2013 by Chapman and Hall/CRC

  3. Generalized Estimating Equations, Second Edition

    By James W. Hardin, Joseph M. Hilbe

    Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models....

    Published December 10th 2012 by Chapman and Hall/CRC

  4. Generalized Linear Mixed Models

    Modern Concepts, Methods and Applications

    By Walter W. Stroup

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how...

    Published September 24th 2012 by CRC Press

  5. Confidence Intervals for Proportions and Related Measures of Effect Size

    By Robert G. Newcombe

    Series: Chapman & Hall/CRC Biostatistics Series

    Confidence Intervals for Proportions and Related Measures of Effect Size illustrates the use of effect size measures and corresponding confidence intervals as more informative alternatives to the most basic and widely used significance tests. The book provides you with a deep understanding of what...

    Published August 25th 2012 by CRC Press

  6. Applied Categorical and Count Data Analysis

    By Wan Tang, Hua He, Xin M. Tu

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach...

    Published June 4th 2012 by Chapman and Hall/CRC

  7. Generalized Linear Models and Extensions, Third Edition

    By James W. Hardin, Joseph M. Hilbe

    This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models...

    Published June 4th 2012 by Stata Press

  8. Multivariate Survival Analysis and Competing Risks

    By Martin J. Crowder

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions...

    Published April 17th 2012 by Chapman and Hall/CRC

  9. Flexible Imputation of Missing Data

    By Stef van Buuren

    Series: Chapman & Hall/CRC Interdisciplinary Statistics

    Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science—multiple imputation—fills gaps in the data with plausible values, the uncertainty of which is coded in the data...

    Published March 29th 2012 by Chapman and Hall/CRC

  10. Time Series Modeling of Neuroscience Data

    By Tohru Ozaki

    Series: Chapman & Hall/CRC Interdisciplinary Statistics

    Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required....

    Published January 26th 2012 by CRC Press