A closed-form matrix regularization parameter for ℓ1-regularized Gaussian MLE, obtained by fixing reselection probability of nonzero entries, matches CV accuracy and support recovery at far lower cost.
hub
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Prop- erties
8 Pith papers cite this work, alongside 9,196 external citations. Polarity classification is still indexing.
8
Pith papers citing it
9,196
external citations · OpenAlex
hub tools
representative citing papers
SALM converges globally to M-stationary points under PLCQ when the nonsmooth term is locally Lipschitz continuous, with a counterexample and numerical evidence on sparse portfolio problems.
HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
PDE-STRIDE applies stability-based model selection to sparse regression for robust, parameter-free recovery of PDEs from noisy data.
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
citing papers explorer
-
Tests for Independence of High-Dimensional Nonstationary Time Series
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.