Results 21 to 30 of about 2,762 (131)
Convex and non-convex regularization methods for spatial point processes intensity estimation [PDF]
, 2017 This paper deals with feature selection procedures for spatial point processes intensity estimation. We consider regularized versions of estimating equations based on Campbell theorem derived from two classical functions: Poisson likelihood and logistic ...
Choiruddin, Achmad +2 more
core +5 more sources
Regularization for Cox's proportional hazards model with NP-dimensionality
, 2012 High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection.
Bradic, Jelena +2 more
core +1 more source
Simulation Study for Variable Selection in Quantile Regression
Wasit Journal for Pure SciencesSix essential techniques for variable selection in quantile regression models are thoroughly examined in this work. Quantile Lasso, gamma-divergence, quantile elastic net, quantile adaptive Lasso, quantile SCAD, and quantile MCP are some of these ...
HUSSEIN HASHEM
doaj +1 more source
On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator
Econometrics, 2019 This paper investigates the asymptotic properties of a penalized empirical likelihood estimator for moment restriction models when the number of parameters ( p n ) and/or the number of moment restrictions increases with the sample size.
Tomohiro Ando, Naoya Sueishi
doaj +1 more source
Adaptive robust variable selection
, 2014 Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted $L_1 ...
Barut, Emre +2 more
core +1 more source
, 2011 A number of variable selection methods have been proposed involving nonconvex penalty functions. These methods, which include the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP), have been demonstrated to have ...
Breheny, Patrick, Huang, Jian
core +1 more source
Subgroup Identification via Multiple Change Point Detection: Methods and Applications
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Subgroup identification methods facilitate the discovery of clinically meaningful subpopulations with differing disease progression, improving personalized risk assessment and treatment strategies. ABSTRACT Subgroup identification is a significant research area in statistics and machine learning, aiming to partition a heterogeneous population into more
Yaguang Li +3 more
wiley +1 more source
IEEE AccessThis paper proposes a new smoothly clipped absolute deviation (SCAD) regularized recursive identification algorithm for nonlinear Hammerstein systems having a finite duration impulse response (FIR) linear part in impulsive noise environment.
Jianqiang Lin, Shing-Chow Chan
doaj +1 more source
, 2014 We consider approaches for improving the efficiency of algorithms for fitting nonconvex penalized regression models such as SCAD and MCP in high dimensions. In particular, we develop rules for discarding variables during cyclic coordinate descent.
Breheny, Patrick, Lee, Sangin
core +2 more sources
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.This systematic review maps 50 years of regression methodology, tracing its evolution from ridge stabilization and penalized estimation to Bayesian, ensemble, and explainable‐AI frameworks. The analysis reveals a unified progression toward interpretable, cross‐domain modeling that integrates statistical rigor, computational scalability, and epistemic ...
Michael Bendersky, Shimon Fridkin
wiley +1 more source