hierarchical

Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities
$74.95
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.

This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems.

The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.
The authors apply principles of hierarchical modeling to ecological problems, including

* occurrence or occupancy models for estimating species distribution
* abundance models based on many sampling protocols, including distance sampling
* capture-recapture models with individual effects
* spatial capture-recapture models based on camera trapping and related methods
* population and metapopulation dynamic models
* models of biodiversity, community structure and dynamics

* Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)

* Development of classical, likelihood-based procedures for inference, as well as
Bayesian methods of analysis

* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS

* Computing support in technical appendices in an online companion web site

Codex Alimentarius: The UN Plan to Eradicate Organic Farming and Destroy the Natural Health Industry
$9.94

Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (Interdisciplinary Statistics)
$79.95
Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis?Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation. Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.

HLM 6: Hierarchical Linear and Nonlinear Modeling
$45.00
This book offers bare necessities to conduct analysis on your own. For those interested in approach on how to teach students to use and understand this concept, this book is not the happiest choice. Its textual style resembles more to a "users guide for technical appliance". Otherwise, useful in its intent.