A Binary Regression Adaptive Goodness-of-fit Test (BAGofT)

11/08/2019 ∙ by Jiawei Zhang, et al. ∙ 24

The Pearson's χ^2 test and residual deviance test are two classical goodness-of-fit tests for binary regression models such as logistic regression. These two tests cannot be applied when we have one or more continuous covariates in the data, a quite common situation in practice. In that case, the most widely used approach is the Hosmer-Lemeshow test, which partitions the covariate space into groups according to quantiles of the fitted probabilities from all the observations. However, its grouping scheme is not flexible enough to explore how to adversarially partition the data space in order to enhance the power. In this work, we propose a new methodology, named binary regression adaptive grouping goodness-of-fit test (BAGofT), to address the above concern. It is a two-stage solution where the first stage adaptively selects candidate partitions using "training" data, and the second stage performs χ^2 tests with necessary corrections based on "test" data. A proper data splitting ensures that the test has desirable size and power properties. From our experimental results, BAGofT performs much better than Hosmer-Lemeshow test in many situations.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 24

page 25

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.