Ecological statistics: contemporary theory and application
Edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, and Vinicio J. Sosa
Table of contents:
Vinicio J. Sosa, Simoneta Negrete-Yankelevich, and Gordon A. Fox:
Introduction
1: Michael A. McCarthy: Approaches to Statistical Inference
2: Earl D. McCoy: Having the Right Stuff: the Effects of Data Constraints on
Ecological Data Analysis
3: Shane A. Richards: Likelihood and Model Selection
4: Shinichi Nakagawa: Missing Data: Mechanisms, Methods and Messages
5: Gordon A. Fox: What You Don't Know Can Hurt You: Censored and Truncated
Data in Ecological Research
6: Yvonne M. Buckley: Generalized Linear Models
7: Bruce E. Kendall: A Statistical Symphony: Instrumental Variables Reveal
Causality and Control Measurement Error
8: James B. Grace, Samuel M. Scheiner, and Donald R. Schoolmaster, Jr.:
Structural Equation Modeling: Building and Evaluating Causal Models
9: Jessica Gurevitch and Shinichi Nakagawa: Research Synthesis Methods in
Ecology
10: Simoneta Negrete-Yankelevich and Gordon A. Fox: Spatial Variation and
Linear Modeling of Ecological Data
11: Marc J. Lajeunesse and Gordon A. Fox: Statistical Approaches to the
Problem of Phylogenetically Correlated Data
12: Jonathan R. Rhodes: Mixture Models for Overdispersed Data
13: Benjamin M. Bolker: Linear and Generalized Linear Mixed Models
Appendix
This novel book synthesizes a number of developments and changes in both our
understanding and practice of ecological statistics, addressing key
approaches and issues that tend to be overlooked in other books such as
missing/censored data, correlation structure of data, heterogeneous data,
and complex causal relationships. These issues characterize a large
proportion of ecological data, but most ecologists' training in traditional
statistics simply does not provide them with adequate preparation to handle
the associated challenges. Uniquely, Ecological Statistics highlights the
underlying links among many statistical approaches that attempt to tackle
these issues. In particular, it gives readers an introduction to approaches
to inference, likelihoods, generalized linear (mixed) models, spatially or
phylogenetically-structured data, and data synthesis, with a strong emphasis
on conceptual understanding and subsequent application to data analysis.
Written by a team of practicing ecologists, mathematical explanations have
been kept to the minimum necessary. This user-friendly textbook will be
suitable for graduate students, researchers, and practitioners in the fields
of ecology, evolution, environmental studies, and computational biology who
are interested in updating their statistical tool kits. A companion web site
provides example data sets and commented code in the R language.
More information about the book can be found at http://www.oup.com/us,
http://Amazon.com, http://bn.com, or your local bookstore.