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arXiv · 1909.00002

Minimum $L^q$-distance estimators for non-normalized parametric models

Steffen Betsch·Bruno Ebner·Bernhard Klar
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Abstract

We propose and investigate a new estimation method for the parameters of models consisting of smooth density functions on the positive half axis. The procedure is based on a recently introduced characterization result for the respective probability distributions, and is to be classified as a minimum distance estimator, incorporating as a distance function the $L^q$-norm. Throughout, we deal rigorously with issues of existence and measurability of these implicitly defined estimators. Moreover, we provide consistency results in a common asymptotic setting, and compare our new method with classical estimators for the exponential-, the Rayleigh-, and the Burr Type XII distribution in Monte Carlo simulation studies. We also assess the performance of different estimators for non-normalized models in the context of an exponential-polynomial family.

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Steffen Betsch, Bruno Ebner, Bernhard Klar. 2020-03-13. Minimum $L^q$-distance estimators for non-normalized parametric models. https://doi.org/10.1002/cjs.11574

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