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Model checking for generalized linear models: a dimension-reduction model-adaptive approach
Methodology · Submitted 9 May 2014
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The test behaves like a local smoothing test as if the model were univariate, and can be consistent against any global alternatives and can detect local alternatives distinct from the null at a fast rate that existing local smoothing tests can achieve only when the model is univariate.
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The abstract, sentence by sentence
Local smoothing testing that is based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. However, relevant tests suffer from the typical curse of dimensionality resulting in slow convergence rates to their limits under the null hypotheses and less deviation from the null under alternatives. This problem leads tests to not well maintain the significance level and to be less sensitive to alternatives. In this paper, a dimension-reduction model-adaptive test is proposed for generalized linear models. The test behaves like a local smoothing test as if the model were univariate, and can be consistent against any global alternatives and can detect local alternatives distinct from the null at a fast rate that existing local smoothing tests can achieve only when the model is univariate. Simulations are carried out to examine the performance of our methodology. A real data analysis is conducted for illustration. The method can readily be extended to global smoothing methodology and other testing problems.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Local smoothing testing that is based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | However, relevant tests suffer from the typical curse of dimensionality resulting in slow convergence rates to their limits under the null hypotheses and less deviation from the null under alternatives. | background 0.84 | 0.06 | 0.03 | asserted 1.00 |
| s2 | This problem leads tests to not well maintain the significance level and to be less sensitive to alternatives. | background 0.90 | 0.08 | 0.03 | asserted 0.99 |
| s3 | In this paper, a dimension-reduction model-adaptive test is proposed for generalized linear models. | contribution 0.99 | 0.89 | 0.17 | asserted 1.00 |
| s4 | The test behaves like a local smoothing test as if the model were univariate, and can be consistent against any global alternatives and can detect local alternatives distinct from the null at a fast rate that existing local smoothing tests can achieve only when the model is univariate. | result 0.64 | 0.81 | 0.86 | asserted 1.00 |
| s5 | Simulations are carried out to examine the performance of our methodology. | method 0.82 | 0.95 | 0.02 | asserted 0.72 |
| s6 | A real data analysis is conducted for illustration. | method 0.57 | 0.93 | 0.03 | asserted 0.96 |
| s7 | The method can readily be extended to global smoothing methodology and other testing problems. | future work 0.95 | 0.29 | 0.07 | asserted 0.67 |
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The paper
Original abstract
Local smoothing testing that is based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. However, relevant tests suffer from the typical curse of dimensionality resulting in slow convergence rates to their limits under the null hypotheses and less deviation from the null under alternatives. This problem leads tests to not well maintain the significance level and to be less sensitive to alternatives. In this paper, a dimension-reduction model-adaptive test is proposed for generalized linear models. The test behaves like a local smoothing test as if the model were univariate, and can be consistent against any global alternatives and can detect local alternatives distinct from the null at a fast rate that existing local smoothing tests can achieve only when the model is univariate. Simulations are carried out to examine the performance of our methodology. A real data analysis is conducted for illustration. The method can readily be extended to global smoothing methodology and other testing problems.
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