Related Experiment Video
Updated: Jul 7, 2026

A Real-world What-Where-When Memory Test
Published on: May 16, 2017
Misspecification tests for binomial and beta-binomial models
Marinela Capanu1, Brett Presnell
1Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. capanum@mskcc.org
Abstract:
The IOS test of Presnell and Boss (J. Am. Stat. Assoc. 2004; 99(465):216-227) is a general-purpose goodness-of-fit test based on the ratio of in-sample and out-of-sample likelihoods. For large samples, the IOS statistic can be approximated by a multiplicative contrast between two estimates of the information matrix, and in this way the IOS test is connected to White's (Econometrica 1982; 50:1-26) information matrix test, or IM test, which is based directly on the difference of two estimates of the information matrix. In this paper, we compare the performance of IOS to that of the IM test and of other goodness-of-fit tests for binomial and beta-binomial models, in both examples and simulations. Our findings suggest that IOS is strongly competitive, not only against the IM test but also against tests designed for specific binomial and beta-binomial models.
Related Concept Videos
Binomial Probability Distribution
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Expected Frequencies in Goodness-of-Fit Tests
Errors In Hypothesis Tests
Bonferroni Test
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Significance Testing: Overview
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

