Durbin’s random substitution and conditional Monte Carlo

Autor:	José M. González-Barrios, Raúl Rueda, Federico J. O'Reilly
Rok vydání:	2009
Předmět:	Statistics and Probability Durbin test Conditional monte carlo Goodness of fit Simple (abstract algebra) Substitution (logic) Bayesian probability Test statistic Statistics Probability and Uncertainty Likelihood principle Algorithm Mathematics
Zdroj:	Metrika. 72:369-383
ISSN:	1435-926X 0026-1335
DOI:	10.1007/s00184-009-0258-z
Popis:	Durbin (Biometrika 48:41–55, 1961) proposed a method called random substitution, by which a composite problem of goodness-of-fit can be reduced to a simple one. In this paper we provide a method of finding the p-value of any test statistic, for a composite goodness-of-fit problem, based on the simulation of a large number of conditional samples, using an analog of Durbin’s proposal in a reverse-type application. We analyze a Bayesian chi-square test proposed in Johnson (Ann Stat 32:2361–2384, 2004) which relies on a single randomization and relate it with Durbin’s original method. We also review a related proposal for conditional Monte-Carlo simulation in Lindqvist and Taraldsen (Biometrika 92:451–464, 2005) and compare it with our procedure. We show our method in a non-group example introduced in Lindqvist and Taraldsen (Biometrika 90:489–490, 2003).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d05e3f6baff9d2b1e81720302e7fc5fc https://doi.org/10.1007/s00184-009-0258-z Zobrazit plný text záznamu Full text from SpringerLink