Abstract
The subject of statistical inference under order restrictions has been studied extensively since Bartholomew’s likelihood-ratio test for means under restricted alternatives [1]. Order restrictions explicitly introduce scientific knowledge into the mathematical formulation of the problem, which can improve inference.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bartholomew, D.: A test of homogeneity of means under restricted alternatives. Journal of the Royal Statistical Society B, 23, 239–281 (1961)
Barlow, R.E., Bartholomew, D.J., Bremner, J.M., Brunk, H.D.: Statistical Inference under Order Restrictions. The Theory and Application of Isotonic Regression. New York, Wiley (1972)
Bayarri, M., Garcia-Donato, G.: Extending conventional priors for testing general hypotheses in linear models. Biometrika, 94, 135–152 (2007)
Berger, J., Pericchi, R.: Objective Bayesian methods for model selection: Introduction and comparison. In: Lahiri, P. (ed) Model Selection. Beachwood, OH, Institute of Mathematical Statistics Lecture Notes Monograph Series 38, 135–207 (2001)
Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis. New York, Wiley (1992)
Dunson, D., Neelon, B.: Bayesian inference on order-constrained parameters in generalized linear models. Biometrics, 59, 286–295 (2003)
Gelman, A., Carlin, J., Stern, H., Rubin, D.: Bayesian Data Analysis (2nd ed.). Chapman & Hall (2004)
Gelfand, A., Smith, A., Lee, T.: Bayesian analysis of constrained parameter and truncated data problems using gibbs sampling. Journal of the American Statistical Association, 87, 523–532 (1992)
Hayter, A.: A one-sided studentized range test for testing against a simple ordered alternative. Journal of the American Statistical Association, 85, 778–785 (1990)
Hayter, A., Liu, W.: Exact calculations for the one-sided studentized range test for testing against a simple ordered alternative. Computational Statistics and Data Analysis, 22, 17–25 (1996)
Huntjens, R.J.C., Peters, M.L., Woertman, L., Bovenschen, L.M., Martin, R.C., Postma, A.: Inter-identity amnesia in dissociative identity disorder: A simulated memory impairment? Psychological Medicine, 36, 857–863 (2006)
Johnson, V.: Bayes factors based on test statistics. Journal of the Royal Statistical Society B, 67, 689–701 (2005)
Klugkist, I., Kato, B., Hoijtink, H.: Bayesian model selection using encompassing priors. Statistica Neerlandica, 59, 57–69 (2005)
Klugkist, I., Laudy, O., Hoijtink, H.: Inequality constrained analysis of variance: A Bayesian approach. Psychological Methods, 10, 477–493 (2005)
Kraemer, H., Paik, M.: A central t approximation to the noncentral t distribution. Technometrics, 21, 357–360 (1979)
Martin, A.D., Quinn, K.M., Park, J.H.: MCMCpack: Markov chain Monte Carlo (MCMC) Package (2007). R package version 0.9-2
Mack, G., Wolfe, D.: K-sample rank test for umbrella alternatives. Journal of the American Statistical Association, 76, 175–181 (1981)
Owen, D.: A special case of a bivariate non-central t-distribution. Biometrika, 52, 437–446 (1965)
Robertson, T., Wright, F., Dykstra, R.: Order Restricted Statistical Inference. New York, Wiley (1988)
Silvapulle, M., Sen, P.: Constrained Statistical Inference. New York, Wiley (2004)
Singh, B., Wright, F.: The level probabilities for a simple loop ordering. Annals of the Institute of Statistical Mathematics, 45, 279–292 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Rossell, D., Baladandayuthapani, V., Johnson, V.E. (2008). Bayes Factors Based on Test Statistics Under Order Restrictions. In: Hoijtink, H., Klugkist, I., Boelen, P.A. (eds) Bayesian Evaluation of Informative Hypotheses. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09612-4_6
Download citation
DOI: https://doi.org/10.1007/978-0-387-09612-4_6
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-09611-7
Online ISBN: 978-0-387-09612-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)