Translator Disclaimer
2013 Deconvolution estimation of mixture distributions with boundaries
Mihee Lee, Peter Hall, Haipeng Shen, J. S. Marron, Jon Tolle, Christina Burch
Electron. J. Statist. 7: 323-341 (2013). DOI: 10.1214/13-EJS774


In this paper, motivated by an important problem in evolutionary biology, we develop two sieve type estimators for distributions that are mixtures of a finite number of discrete atoms and continuous distributions under the framework of measurement error models. While there is a large literature on deconvolution problems, only two articles have previously addressed the problem taken up in our article, and they use relatively standard Fourier deconvolution. As a result the estimators suggested in those two articles are degraded seriously by boundary effects and negativity. A major contribution of our article is correct handling of boundary effects; our method is asymptotically unbiased at the boundaries, and also is guaranteed to be nonnegative. We use roughness penalization to improve the smoothness of the resulting estimator and reduce the estimation variance. We illustrate the performance of the proposed estimators via our real driving application in evolutionary biology and two simulation studies. Furthermore, we establish asymptotic properties of the proposed estimators.


Download Citation

Mihee Lee. Peter Hall. Haipeng Shen. J. S. Marron. Jon Tolle. Christina Burch. "Deconvolution estimation of mixture distributions with boundaries." Electron. J. Statist. 7 323 - 341, 2013.


Published: 2013
First available in Project Euclid: 28 January 2013

zbMATH: 1337.62068
MathSciNet: MR3020423
Digital Object Identifier: 10.1214/13-EJS774

Primary: 62G08, 62H25
Secondary: 65F30

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society


Back to Top