Results 91 to 100 of about 1,354,633 (198)

Unraveling Similarities and Differences Between Non-Negative Garrote and Adaptive Lasso: A Simulation Study in Low- and High-Dimensional Data

Stats
Penalized regression methods are widely used for variable selection. Non-negative garrote (NNG) was one of the earliest methods to combine variable selection with shrinkage of regression coefficients, followed by lasso.
Edwin Kipruto, Willi Sauerbrei
doaj   +1 more source

Pre-Processing Ensemble Modeling Based on Faster Covariate Selection Calibration for Near-Infrared Spectroscopy

Applied Sciences
Ensemble techniques are crucial for preprocessing near-infrared (NIR) data, yet effectively integrating information from multiple preprocessing methods remains challenging.
Yonghong Wu, Yukun Zhou, Xiaojing Chen, Zhonghao Xie, Shujat Ali, Guangzao Huang, Leiming Yuan, Wen Shi, Xin Wang, Lechao Zhang   +9 more
doaj   +1 more source

A taylor series based input variable selection for nonlinear system

, 2005
Input variable selection is always a significant problem in nonlinear system modeling. In this paper, we propose an effective and efficient input variable selection method based on Taylor series.
Li DQ(李德强), Song GW(宋广为)
core  

Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy

Agronomy
This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy.
Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song, Yanlei Xu   +5 more
doaj   +1 more source

Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models

Applied Sciences
Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical
Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke, Barbara Więckowska   +3 more
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Variable selection through CART [PDF]

, 2006
This paper deals with variable selection in the regression or binary classification frameworks. It proposes an automatic and exhaustive procedure which relies on the use of the CART algorithm and on model selection via penalization.
Sauvé, Marie, Tuleau, Christine
core   +1 more source

VAR Forecasting Using Bayesian Variable Selection [PDF]

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and ...
Dimitris Korobilis
core  

glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models

Journal of Statistical Software, 2010
We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and,
Vincent Calcagno, Claire de Mazancourt
doaj  

A rule-of-thumb for the variable bandwidth selection in kernel hazard rate estimation [PDF]

In nonparametric curve estimation the decision about the type of smoothing parameter is critical for the practical performance. The nearest neighbor bandwidth as introduced by Gefeller and Dette 1992 for censored data in survival analysis is specified by
Weißbach, Rafael, Gefeller, Olaf
core  

Controlling Variable Selection By the Addition of Pseudo-Variables

, 2004
Many variable selection procedures have been developed in the literature for linear regression models. We propose a new and general approach, the False Selection Rate (FSR) method, to control variable selection with the advantage of being applicable to a
Wu, Yujun
core