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Research Article Open access CC BY 4.0

A Simulation Study for the AIC and Likelihood Cross-validation: The Case of Exponential Versus Weibull Distributions

Kunio Takezawa

Journal of Advances in Mathematics and Computer Science · pp. 1–13 · Published 13 Aug 2018

10.9734/JAMCS/2018/43344

Abstract

Various methods are available for choosing statistical models. It is difficult to know which model selection criterion is the best for specific data. This paper discusses a method for choosing the model selection criterion based on the characteristics of the data and models. As an example, we examined the choice between AIC and likelihood cross-validation as the model selection criterion with the exponential distribution and Weibull distribution as candidate models. First, we examined the characteristics of AIC and likelihood cross-validation using data generated from an exponential distribution or Weibull distribution; AIC and likelihood cross-validation show substantially different natures. Next, from the results of the numerical simulations, we propose an intuitive method for deciding whether to use AIC or likelihood cross-validation.

AIC cross-validation expected log-likelihood future data exponential distribution maxi-mum likelihood estimator Weibull distribution

References (10)

  1. 1 Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach [DOI]
  2. 2 Information Theory and an Extension of the Maximum Likelihood Principle [DOI]
  3. 3 Cross-Validatory Choice and Assessment of Statistical Predictions [DOI]
  4. 4 The Predictive Sample Reuse Method with Applications [DOI]
  5. 5 An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion [DOI]
  6. 6 Information Criteria and Statistical Modeling [DOI]
  7. 7 Model-structure selection by cross-validation [DOI]
  8. 8 Learning Regression Analysis by Simulation [DOI]
  9. 9 Asymptotic biases of information and cross-validation criteria under canonical parametrization [DOI]
  10. 10 A Review of Cross Validation and Adaptive Model Selection [DOI]

Cited by 1

Testing the Fairness of a Coin by Akaike's Information Criterion

K. Takezawa · Journal of Advances in Mathematics and Computer Science · 2019

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