Statistics and Computing

, Volume 23, Issue 2, pp 149–162

A nonparametric assessment of model adequacy based on Kullback-Leibler divergence

Article

DOI: 10.1007/s11222-011-9298-0

Cite this article as:
Hsieh, PH. Stat Comput (2013) 23: 149. doi:10.1007/s11222-011-9298-0

Abstract

A discrepancy measure to assess model fitness against a nonparametric alternative is proposed. First, a Polya tree prior is constructed so that the centering distribution is the null. Second, the prior is updated in the light of data to obtain the posterior centering distribution as the alternative. Third, a Kullback-Leibler divergence type of test statistic is derived to assess the discrepancy between the two centering distributions. The properties of the test statistic are derived, and a power comparison with several well-known test statistics is conducted. The use of the test statistic is illustrated using network traffic data.

Keywords

Goodness of fit Nonparametric alternative Packet train Polya tree Teletraffic data 

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.College of BusinessOregon State UniversityCorvallisUSA