Constructing irregular histograms by penalized likelihood
| dc.contributor.author | Gather, Ursula | |
| dc.contributor.author | Mildenberger, Thoralf | |
| dc.contributor.author | Rozenholc, Yves | |
| dc.date.accessioned | 2009-04-30T10:42:23Z | |
| dc.date.available | 2009-04-30T10:42:23Z | |
| dc.date.issued | 2009-04-30T10:42:23Z | |
| dc.description.abstract | We propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent work in model selection by Castellan [1] and Massart [2]. We give a complete description of the algorithm and a proper tuning of the penalties. Finally, we compare our procedure to other existing proposals for a wide range of different densities and sample sizes. [1] Castellan, G., 1999. Modified Akaike's criterion for histogram density estimation. Technical Report 99.61, Université de Paris-Sud. [2] Massart, P., 2007. Concentration inequalities and model selection. Lecture Notes in Mathematics Vol. 1896, Springer, New York. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/26096 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-587 | |
| dc.language.iso | en | de |
| dc.subject | density estimation | en |
| dc.subject | dynamic programming | en |
| dc.subject | irregular histogram | en |
| dc.subject | penalized likelihood | en |
| dc.subject.ddc | 004 | |
| dc.title | Constructing irregular histograms by penalized likelihood | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |