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.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Prop- erties
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
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
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The Regularization Parameter: Sparse Precision Matrix Estimation
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.
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Convergence of the Safeguarded Augmented Lagrangian Method under the Polyak-Lojasiewicz constraint qualification for Constrained Composite Optimization
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.
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Hierarchical excitatory processes for modelling event-time data in the presence of exogenous stimuli
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.
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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.
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Stability selection enables robust learning of partial differential equations from limited noisy data
PDE-STRIDE applies stability-based model selection to sparse regression for robust, parameter-free recovery of PDEs from noisy data.
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A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection
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.
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Constrained Weighted Bayesian Bootstrap
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
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Factor multivariate stochastic volatility models of high dimension
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.