A Bayesian approach to nonparametric test problems
type: Item
instance of: scholarly article
title: A Bayesian approach to nonparametric test problems
zbMATH Open document ID: 0673.62036
DOI: 10.1007/BF00053067
author: Yosiyuki Sakamoto
author: Makio Ishiguro
published in: Annals of the Institute of Statistical Mathematics
publication date: 1988-01-01T00:00:00Z
review text: We propose an alternative approach to the classical ``nonparametric'' test problems, such as the goodness of fit test and the two-sample nonparametric test. In this approach, those problems are reviewed from the viewpoint of the estimation of the underlying population distributions and are formulated as the problem of model selection between Bayesian models. The model selection can be easily realized by choosing a model with the smallest ABIC, Akaike Bayesian information criterion. The approach provides the estimates of the density of the underlying population distribution(s) of any shape as well as the evaluation of the goodness of fit or the check of homogeneity of distributions.
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 4102301
zbMATH Keywords: smoothing prior
zbMATH Keywords: density estimator
zbMATH Keywords: AIC
zbMATH Keywords: multinomial logistic
zbMATH Keywords: transformation
zbMATH Keywords: B-spline
zbMATH Keywords: goodness of fit test
zbMATH Keywords: two-sample nonparametric test
zbMATH Keywords: model selection
zbMATH Keywords: Bayesian models
zbMATH Keywords: ABIC
zbMATH Keywords: Akaike Bayesian information criterion
zbMATH Keywords: check of homogeneity of distributions
MaRDI profile type: Publication
cites work: Q4769776
cites work: Q4170056
cites work: SEASONAL ADJUSTMENT BY A BAYESIAN MODELING
cites work: Q3747471
full work available at URL: https://doi.org/10.1007/bf00053067
OpenAlex ID: W2084829609
description: scientific article; zbMATH DE number 4102301