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Evaluating the relative merits of competing models based on empirical likelihood ratio test

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  • Yan Fan
  • Yukun Liu

Abstract

Competing models arise naturally in many research fields, such as survival analysis and economics, when the same phenomenon of interest is explained by different researcher using different theories or according to different experiences. The model selection problem is therefore remarkably important because of its great importance to the subsequent inference; Inference under a misspecified or inappropriate model will be risky. Existing model selection tests such as Vuong's tests [26] and Shi's non-degenerate tests [21] suffer from the variance estimation and the departure of the normality of the likelihood ratios. To circumvent these dilemmas, we propose in this paper an empirical likelihood ratio (ELR) tests for model selection. Following Shi [21], a bias correction method is proposed for the ELR tests to enhance its performance. A simulation study and a real-data analysis are provided to illustrate the performance of the proposed ELR tests.

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  • Yan Fan & Yukun Liu, 2016. "Evaluating the relative merits of competing models based on empirical likelihood ratio test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2595-2607, October.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2595-2607
    DOI: 10.1080/02664763.2016.1142944
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