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Monte Carlo and Example-Based Insights

  • Kenneth P. Burnham
  • David R. Anderson

Abstract

This chapter gives results from some illustrative exploration of the performance of information-theoretic criteria for model-selection and methods to quantify precision when there is model-selection uncertainty. The methods given in Chapter 4 are illustrated and additional insights are provided based on simulation and real data. Section 5.2 utilizes a chain binomial survival model for some Monte Carlo evaluation of unconditional sampling variance estimation, confidence intervals, and model averaging. For this simulation the generating process is known and can be of relatively high dimension. The generating model and the models used for data analysis in this chain binomial simulation are easy to understand and have no nuisance parameters. We give some comparisons of AIC versus BIC selection and use achieved confidence interval coverage as an integrating metric to judge the success of various approaches to inference.

Keywords

Akaike Weight Monte Carlo Result Line Transect Sampling Confidence Interval Coverage Unconditional Coverage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Kenneth P. Burnham
    • 1
  • David R. Anderson
    • 1
  1. 1.Colorado Cooperative Fish and Wildlife Research UnitColorado State UniversityFort CollinsUSA

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