Ultra-High Dimensional Single-Index Quantile Regression
Bibliographic record
Abstract
We consider a flexible semiparametric single-index quantile regression model where the number of covariates may be ultra-high dimensional, and the number of the relevant covariates is potentially diverging. The approach is particularly appealing to uncover the complex heterogeneity in high-dimensional data, incorporate nonlinearity and potential interaction, avoid the curse of dimensionality, and allow different variables to be included at different quantile levels. We estimate the unknown function via polynomial splines nonparametrically and adopt a nonconvex penalty function to identify the sparse variable set. We further extend it to partially linear single-index quantile model where both the single-index components in the nonparametric term and the partially linear component scan be in ultra-high dimension. However, a number of major challenges arise in developing both theory and computation: (a) The model is highly nonlinear in single-index coefficients because the high-dimensional single-index covariates are embedded inside the unknown flexible function. (b) The data are ultra-high dimensional where the dimension of the single-index covariates (Pn) is diverging or even in the exponential order of sample size n. (c) The objective function is non-smooth for quantile regression. (d) Nonconvex variable selection such as SCAD is adopted for regularization. (e) The extended partially linear single-index quantile models may include both ultra-high dimensional (Pn) single-index covariates and ultra-high dimensional (qn) partially linear covariates. We develop a novel approach using empirical process techniques in establishing the theoretical properties of the nonconvex penalized estimators for partially linear single-index quantile models and show those estimators indeed possess the oracle property in ultra-high dimensional setting. We propose an efficient algorithm to circumvent the computational challenges. The results of Monte Carlo simulations and an application to gene expression data demonstrate the effectiveness of the proposed models and estimation method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".