<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Non-parametric density estimation with shape restrictions has witnessed a great deal of attention recently. We consider the maximum-likelihood problem of estimating a log-concave density from a given finite set of empirical data and present a computational approach to the resulting optimization problem. Our approach targets the ability to trade-off computational costs against estimation accuracy in order to alleviate the curse of dimensionality of density estimation in higher dimensions.</p></abstract>

Non-parametric density estimation with shape restrictions has witnessed a great deal of attention recently. We consider the maximum-likelihood problem of estimating a log-concave density from a given finite set of empirical data and present a computational approach to the resulting optimization problem. Our approach targets the ability to trade-off computational costs against estimation accuracy in order to alleviate the curse of dimensionality of density estimation in higher dimensions.

A Computational Approach to Log-Concave Density Estimation

Image and Pattern Analysis Group, University of Heidelberg, Speyerer Str. 6, 69126 Heidelberg,

Analele ştiinţifice ale Universităţii "Ovidius" Constanţa. Seria Matematică

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"A Computational Approach to Log-Concave Density Estimation"}

Non-parametric density estimation with shape restrictions has witnessed a great deal of attention recently. We consider the maximum-likelihood problem of...

A Computational Approach to Log-Concave Density Estimation

Online veröffentlicht: 22. Apr. 2017

Seitenbereich: 151 - 166

Eingereicht: 01. Dez. 2014

Akzeptiert: 01. Feb. 2015

DOI: https://doi.org/10.1515/auom-2015-0053

Schlüsselwörter
log-convave distributions, probability density estimation, mathematical programming, machine learning

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A Computational Approach to Log-Concave Density Estimation

Online veröffentlicht: 22. Apr. 2017

Seitenbereich: 151 - 166

Eingereicht: 01. Dez. 2014

Akzeptiert: 01. Feb. 2015

DOI: https://doi.org/10.1515/auom-2015-0053

Schlüsselwörterlog-convave distributions, probability density estimation, mathematical programming, machine learning

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
log-convave distributions, probability density estimation, mathematical programming, machine learning