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What are hierarchical models and how do we analyze them?

January 1, 2016

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 Year 2016
Title What are hierarchical models and how do we analyze them?
DOI 10.1016/B978-0-12-801378-6.00002-3
Authors Andy Royle
Publication Type Book Chapter
Publication Subtype Book Chapter
Index ID 70169909
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center