## Bayesian Statistics (1989)

Venue: | in WWW', Computing Science and Statistics |

Citations: | 20 - 0 self |

### BibTeX

@INPROCEEDINGS{Lee89bayesianstatistics,

author = {Hyunsook Lee and L. Rosenberger},

title = {Bayesian Statistics},

booktitle = {in WWW', Computing Science and Statistics},

year = {1989},

pages = {485--489},

publisher = {University Press}

}

### Years of Citing Articles

### OpenURL

### Abstract

∗ Signatures are on file in the Graduate School. This dissertation presents two topics from opposite disciplines: one is from a parametric realm and the other is based on nonparametric methods. The first topic is a jackknife maximum likelihood approach to statistical model selection and the second one is a convex hull peeling depth approach to nonparametric massive multivariate data analysis. The second topic includes simulations and applications on massive astronomical data. First, we present a model selection criterion, minimizing the Kullback-Leibler distance by using the jackknife method. Various model selection methods have been developed to choose a model of minimum Kullback-Liebler distance to the true model, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), and Bootstrap information criterion. Likewise, the jackknife method chooses a model of minimum Kullback-Leibler distance through bias reduction. This bias, which is inevitable in model