The following pages link to (Q4864293):
Displaying 50 items.
- Rank reducible varying coefficient model (Q1007481) (← links)
- Bayesian sigmoid shrinkage with improper variance priors and an application to wavelet denois\-ing (Q1010462) (← links)
- A simple approach for varying-coefficient model selection (Q1015855) (← links)
- Block-coordinate gradient descent method for linearly constrained nonsmooth separable optimization (Q1016411) (← links)
- Relaxed maximum a posteriori fault identification (Q1016851) (← links)
- Positive shrinkage, improved pretest and absolute penalty estimators in partially linear models (Q1017635) (← links)
- A simple forward selection procedure based on false discovery rate control (Q1018611) (← links)
- Parsimonious additive models (Q1019916) (← links)
- Knot selection by boosting techniques (Q1020124) (← links)
- Smooth functions and local extreme values (Q1020189) (← links)
- Boosting ridge regression (Q1020707) (← links)
- Support vector machines with adaptive \(L_q\) penalty (Q1020744) (← links)
- Efficient algorithms for computing the best subset regression models for large-scale problems (Q1020780) (← links)
- Robust variable selection using least angle regression and elemental set sampling (Q1020812) (← links)
- Editorial: Statistical learning methods including dimensionality reduction (Q1020825) (← links)
- Relaxed Lasso (Q1020826) (← links)
- Input selection and shrinkage in multiresponse linear regression (Q1020828) (← links)
- Gaussian model selection with an unknown variance (Q1020973) (← links)
- SCAD-penalized regression in high-dimensional partially linear models (Q1020975) (← links)
- On the degrees of freedom in shrinkage estimation (Q1021833) (← links)
- On the distribution of the adaptive LASSO estimator (Q1022011) (← links)
- Elastic-net regularization in learning theory (Q1023403) (← links)
- Efficient methods for estimating constrained parameters with applications to regularized (Lasso) logistic regression (Q1023690) (← links)
- Subset selection for vector autoregressive processes using Lasso (Q1023702) (← links)
- Predictive performance of Dirichlet process shrinkage methods in linear regression (Q1023703) (← links)
- A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE (Q1023796) (← links)
- EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariatet-distributions (Q1023837) (← links)
- A note on adaptive group Lasso (Q1023903) (← links)
- Regularized simultaneous model selection in multiple quantiles regression (Q1023905) (← links)
- Increasing the usefulness of additive spline models by knot removal (Q1023906) (← links)
- Simultaneous selection of variables and smoothing parameters in structured additive regression models (Q1023927) (← links)
- Selection of components and degrees of smoothing via Lasso in high dimensional nonparametric additive models (Q1023939) (← links)
- On a generalization of the Laplace approximation (Q1026338) (← links)
- An improved model averaging scheme for logistic regression (Q1026355) (← links)
- Asymptotics for argmin processes: convexity arguments (Q1026368) (← links)
- Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients (Q1032124) (← links)
- Some challenges for statistics (Q1039967) (← links)
- Tournament screening cum EBIC for feature selection with high-dimensional feature spaces (Q1042967) (← links)
- High-dimensional additive modeling (Q1043712) (← links)
- The composite absolute penalties family for grouped and hierarchical variable selection (Q1043749) (← links)
- Mixed linear system estimation and identification (Q1046675) (← links)
- Estimating the dimension of a model (Q1247128) (← links)
- Computation of the NPMLE of distribution functions for interval censored and truncated data with applications to the Cox model. (Q1274148) (← links)
- A comparison of regression spline smoothing procedures (Q1424601) (← links)
- Minimax estimation in linear regression under restrictions (Q1587193) (← links)
- The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. With comments by Ronald A. Thisted and M. R. Osborne and a rejoinder by the authors (Q1596137) (← links)
- Robust methods for inferring sparse network structures (Q1615086) (← links)
- Model selection in kernel ridge regression (Q1615122) (← links)
- Interquantile shrinkage and variable selection in quantile regression (Q1615197) (← links)
- Nonnegative-Lasso and application in index tracking (Q1615217) (← links)
