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Monte Carlo and Example-Based Insights

In: Model Selection and Inference

Author

Listed:
  • Kenneth P. Burnham

    (Colorado State University, Colorado Cooperative Fish and Wildlife Research Unit)

  • David R. Anderson

    (Colorado State University, Colorado Cooperative Fish and Wildlife Research Unit)

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.

Suggested Citation

  • Kenneth P. Burnham & David R. Anderson, 1998. "Monte Carlo and Example-Based Insights," Springer Books, in: Model Selection and Inference, chapter 5, pages 159-229, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-2917-7_5
    DOI: 10.1007/978-1-4757-2917-7_5
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