What are hierarchical models and how do we analyze them?
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Abstract
In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | What are hierarchical models and how do we analyze them? |
| DOI | 10.1016/B978-0-12-801378-6.00002-3 |
| Year Published | 2016 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Patuxent Wildlife Research Center |
| Description | 60 p. |
| First page | 19 |
| Last page | 78 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |