Paper Search Console

Home Search Page About Contact

Journal Title

Title of Journal: Comput Stat

Search In Journal Title:

Abbravation: Computational Statistics

Search In Journal Abbravation:

Publisher

Springer-Verlag

Search In Publisher:

DOI

10.1016/0166-1280(82)80022-5

Search In DOI:

ISSN

1613-9658

Search In ISSN:
Search In Title Of Papers:

Bayesian model selection approach to the one way a

Authors: J A Cano C Carazo D Salmerón
Publish Date: 2012/06/26
Volume: 28, Issue: 3, Pages: 919-931
PDF Link

Abstract

An objective Bayesian model selection procedure is proposed for the one way analysis of variance under homoscedasticity Bayes factors for the usual default prior distributions are not well defined and thus Bayes factors for intrinsic priors are used instead The intrinsic priors depend on a training sample which is typically a unique random vector However for the homoscedastic ANOVA it is not the case Nevertheless we are able to illustrate that the Bayes factors for the intrinsic priors are not sensitive to the minimal training sample chosen furthermore we propose an alternative pooled prior that yields similar Bayes factors To compute these Bayes factors Bayesian computing methods are required when the sample sizes of the involved populations are large Finally a one to one relationship—which we call the calibration curve—between the posterior probability of the null hypothesis and the classical p value is found thus allowing comparisons between these two measures of evidence The behavior of the calibration curve as a function of the sample size is studied and conclusions relating both procedures are stated

Keywords:

References

.
Search In Abstract Of Papers:
Other Papers In This Journal:
  1. Bayesian lasso binary quantile regression
  2. Determining p -values for systems cointegration tests with a prior adjustment for deterministic terms
  3. GEVA: geometric variability-based approaches for identifying patterns in data
  4. A note on the prior parameter choice in finite mixture models of distributions from exponential families
  5. Learning vector quantization classifiers for ROC-optimization
  6. Parameter cascades and profiling in functional data analysis
  7. Confidence intervals for the regression parameter based on weighted log-rank estimating functions
  8. Biomarker discovery: classification using pooled samples
  9. Improving efficiency of data augmentation algorithms using Peskun’s theorem
  10. Multivariate elliptically contoured stable distributions: theory and estimation
  11. Implied Trinomial Trees and Their Implementation with XploRe
  12. On testing the log-gamma distribution hypothesis by bootstrap
  13. Bayesian model-based tight clustering for time course data
  14. Smoothing combined generalized estimating equations in quantile partially linear additive models with longitudinal data
  15. A generalized log-normal distribution and its goodness of fit to censored data
  16. A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$
  17. The Monte Carlo EM method for estimating multinomial probit latent variable models
  18. Integrated likelihood computation methods
Search Result: