Conditional quantile screening in ultrahigh-dimensional heterogeneous data

@article{Wu2015ConditionalQS,
  title={Conditional quantile screening in ultrahigh-dimensional heterogeneous data},
  author={Yuanshan Wu and Guosheng Yin},
  journal={Biometrika},
  year={2015},
  volume={102},
  pages={65-76}
}
To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we propose a conditional quantile screening method, which enables us to select features that contribute to the conditional quantile of the response given the covariates. The method can naturally handle censored data by incorporating a weighting scheme through redistribution of the mass to the right; moreover, it is invariant to monotone transformation of the response and requires substantially weaker… 

Figures and Tables from this paper

Variable screening for ultrahigh dimensional censored quantile regression
ABSTRACT Quantile regression is a flexible approach to assessing covariate effects on failure time, which has attracted considerable interest in survival analysis. When the dimension of covariates is
Nonparametric independence feature screening for ultrahigh-dimensional survival data
TLDR
A novel nonparametric feature screening procedure based on ultrahigh-dimensional survival data is developed by incorporating the inverse probability weighting scheme to tackle the issue of censoring.
Robust model-free feature screening for ultrahigh dimensional surrogate data
TLDR
This paper proposes a marginal screening procedure based on the augmented inverse probability weighted methods and the nonparametric imputation technique that utilizes the surrogate information efficiently to overcome the missing data problem.
Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation
TLDR
A new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation (CQC), which not only is robust against outliers, but also can discover the nonlinear relationship between independent variables and censored dependent variable.
Model-free slice screening for ultrahigh-dimensional survival data
TLDR
This work proposes a fused Kolmogorov–Smirnov filter to screen out the irrelevant dependent variables for ultrahigh-dimensional survival data and develops an iterative algorithm to enhance the performance of the method while dealing with the practical situations where some covariates may be marginally unrelated but jointly related to the response.
A new nonparametric screening method for ultrahigh-dimensional survival data
Non-marginal feature screening for additive hazard model with ultrahigh-dimensional covariates
Abstract Survival data with ultrahigh-dimensional covariates have been frequently encountered in medical studies and other fields. In this article, we propose a non-marginal feature screening
...
...

References

SHOWING 1-10 OF 23 REFERENCES
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
TLDR
It is proved that the sure screening property remains valid when the response variable is subject to random right censoring, and the quantile-adaptive framework can naturally handle censored data arising in survival analysis.
Locally Weighted Censored Quantile Regression
TLDR
A new locally weighted censored quantile regression approach that adopts the redistribution-of-mass idea and employs a local reweighting scheme, and establishes the consistency and asymptotic normality of the proposed estimator.
Model-Free Feature Screening for Ultrahigh-Dimensional Data
TLDR
It is demonstrated that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection.
Independent screening for single‐index hazard rate models with ultrahigh dimensional features
Summary.  In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method.
Censored Regression Quantiles
Using quantile regression to analyze survival times offers an valuable complement to traditional Cox proportional hazards modelling. Unfortunately, this approach has been hampered by the lack of a
A Lack-of-Fit Test for Quantile Regression
We propose an omnibus lack-of-fit test for linear or nonlinear quantile regression based on a cusum process of the gradient vector. The test does not involve nonparametric smoothing but is consistent
Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
TLDR
This work shows that with general nonparametric models, under some mild technical conditions, the proposed independence screening methods have a sure screening property and the extent to which the dimensionality can be reduced by independence screening is also explicitly quantified.
Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening
In this article, we propose a new metric, the so-called martingale difference correlation, to measure the departure of conditional mean independence between a scalar response variable V and a vector
Sure independence screening in generalized linear models with NP-dimensionality
TLDR
It is shown that the proposed methods also possess the sure screening property with vanishing false selection rate, which justifies the applicability of such a simple method in a wide spectrum.
...
...