Testing with Exponentially Tilted Empirical Likelihood

A. Felipe, N. Martín, P. Miranda and L. Pardo ()
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A. Felipe: Complutense University of Madrid
N. Martín: Complutense University of Madrid
P. Miranda: Complutense University of Madrid
L. Pardo: Complutense University of Madrid

Methodology and Computing in Applied Probability, 2018, vol. 20, issue 4, 1319-1358

Abstract: Abstract Imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if a subset of restrictions were not correctly specified, the usual test-statistics for correctly specified models tend to reject erronously a simple null hypothesis. In this setting, we may say that the model suffers from misspecification. We study the behavior of empirical phi-divergence test-statistics, introduced in Balakrishnan et al. Statistics 49:951–977 (2015), by using the exponential tilted empirical likelihood estimators of Schennach Ann Stat 35:634–672 (2007), as a good compromise between the efficiency of the significance level for small sample sizes and the robustness under misspecification.

Keywords: Empirical likelihood; Empirical phi-divergence test statistics; Model misspecification; Phi-divergence measures; 62F05; 62F35 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11009-018-9620-9

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