Testing for the presence of significant covariates through conditional marginal regression

Tang, Yanlin; Wang, Huixia Judy; Barut, Emre

doi:10.1093/biomet/asx061

Cited by 3 publications

(12 citation statements)

References 18 publications

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“…The proposed test can be used as a stopping rule in forward regression to discover significant components in X. To account for multiple testing in the sequential procedure, we follow a similar two-stage selection as in Tang et al (2018).…”

Section: Forward Selection Via a Sequential Conditional Testmentioning

confidence: 99%

“…The following tests are compared: (i) four variations of the proposed test, in Tang et al (2018) for the mean model, with the tuning parameter set as…”

Section: Size and Power Studymentioning

confidence: 99%

“…The number of bootstraps is set as 500 for both QME and CAR. In Wang et al (2018a) and Tang et al (2018), the tuning parameter is selected by double bootstrap, which is computationally intensive, so we fix the parameter Statistica Sinica: Newly accepted Paper (accepted author-version subject to English editing) at one value that performs relatively better to save the computation time. Table S.1 in the Supplementary Materials summarizes the average computing time for each method.…”

Section: Size and Power Studymentioning

confidence: 99%

“…Numerical studies show that the proposed test provides adequate control of FWER with competitive power. We demonstrate that the proposed procedures are computationally efficient, taking much less time than those methods that require intensive resampling or double bootstrap (McKeague and Qian, 2015;Wang et al, 2018a;Tang et al, 2018). Different from the proposed maximum-type statistic, Statistica Sinica: Newly accepted Paper (accepted author-version subject to English editing) another common approach to determine group-wise significance is the combination test, for instance, the Cauchy combination test (Liu and Xie, 2019, CCT), which combines the P -values obtained from individual test of each covariate into a single P -value to assess the group-wise significance.…”

Section: Introductionmentioning

confidence: 99%

“…Guo and Chen (2016) proposed to test the overall significance of X conditional on Z, based on a quadratic form of the score functions. Tang et al (2018) proposed a hybrid test of maximum-and sum-squared-type statistics based on conditional marginal regressions, by regressing Y on Z and each X j separately. Based on the sum of powered scores (Pan et al, 2014;Xu et al, 2016), Wu et al (2019) proposed an adaptive test for generalized linear models, by assuming that the errors satisfy the sub-Gaussian condition and p n = o(n 2 ).…”

Section: Introductionmentioning

confidence: 99%

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Conditional Marginal Test for High Dimensional Quantile Regression

Tang¹,

Yin-feng²,

Wang³

et al. 2022

STAT SINICA

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Analysis of tail quantiles of the response distribution is sometimes more important than the mean in biomarker studies. Inference in quantile regression is complicated when there exist a large number of candidate markers together with some pre-specified controlled covariates. In this paper, we develop a new and simple testing procedure to detect the effects of biomarkers in high-dimensional quantile regression in the presence of protected covariates. The test is based on the maximum-score-type statistic obtained from conditional marginal regression. We establish the asymptotic properties of the proposed test statistic under both null and alternative hypotheses, and further propose an alternative multiplier bootstrap method with theoretical justifications. We demonstrate through numerical studies that the proposed method provides adequate controls of the family-wise error rate with competitive power, and it can also be used as a stopping rule in the forward regression. The proposed method is applied to a motivating genome-wide association study

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Section: Forward Selection Via a Sequential Conditional Testmentioning

confidence: 99%

“…The following tests are compared: (i) four variations of the proposed test, in Tang et al (2018) for the mean model, with the tuning parameter set as…”

Section: Size and Power Studymentioning

confidence: 99%

Section: Size and Power Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conditional Marginal Test for High Dimensional Quantile Regression

Tang¹,

Yin-feng²,

Wang³

et al. 2022

STAT SINICA

Self Cite

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show abstract

Score‐based test in high‐dimensional quantile regression for longitudinal data with application to a glomerular filtration rate data

Wang

Tang

2023

Stat

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Motivated by a genome‐wide association study on the glomerular filtration rate, we develop a new robust test for longitudinal data to detect the effects of biomarkers in high‐dimensional quantile regression, in the presence of prespecified control variables. The test is based on the sum of score‐type statistics deduced from conditional quantile regression. The test statistic is constructed in a working‐independent manner, but the calibration reflects the intrinsic within‐subject correlation. Therefore, the test takes advantage of the feature of longitudinal data and provides more information than those based on only one measurement for each subject. Asymptotic properties of the proposed test statistic are established under both the null and local alternative hypotheses. Simulation studies show that the proposed test can control the family‐wise error rate well, while providing competitive power. The proposed method is applied to the motivating glomerular filtration rate data to test the overall significance of a large number of candidate single‐nucleotide polymorphisms that are possibly associated with the Type 1 diabetes, conditioning on the patients' demographics.

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Variable Selection for Generalized Linear Model with Highly Correlated Covariates

Yue,

Wang,

2024

Acta. Math. Sin.-English Ser.

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Testing for the presence of significant covariates through conditional marginal regression

Cited by 3 publications

References 18 publications

Conditional Marginal Test for High Dimensional Quantile Regression

Conditional Marginal Test for High Dimensional Quantile Regression

Score‐based test in high‐dimensional quantile regression for longitudinal data with application to a glomerular filtration rate data

Variable Selection for Generalized Linear Model with Highly Correlated Covariates

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