
Goodnessoffit testing in highdimensional generalized linear models
We propose a family of tests to assess the goodnessoffit of a highdim...
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Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address...
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Testing Goodness of Fit of Conditional Density Models with Kernels
We propose two nonparametric statistical tests of goodness of fit for co...
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Variable Grouping Based Bayesian Additive Regression Tree
Using ensemble methods for regression has been a large success in obtain...
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A maximummeandiscrepancy goodnessoffit test for censored data
We introduce a kernelbased goodnessoffit test for censored data, wher...
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A general approach to detect gene (G)environment (E) additive interaction leveraging GE independence in casecontrol studies
It is increasingly of interest in statistical genetics to test for the p...
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How to avoid the zeropower trap in testing for correlation
In testing for correlation of the errors in regression models the power ...
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A Binary Regression Adaptive Goodnessoffit Test (BAGofT)
The Pearson's χ^2 test and residual deviance test are two classical goodnessoffit 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 HosmerLemeshow 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 goodnessoffit test (BAGofT), to address the above concern. It is a twostage 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 HosmerLemeshow test in many situations.
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