Variable selection in high-dimensional regression: a nonparametric procedure for business failure prediction
Sign Up to like & getrecommendations! Published in 2017 at "Applied Stochastic Models in Business and Industry"
DOI: 10.1002/asmb.2240
Abstract: Business failure prediction models are important in providing warning for preventing financial distress and giving stakeholders time to react in a timely manner to a crisis. The empirical approach to corporate distress analysis and forecasting… read more here.
Keywords: business failure; business; variable selection; failure prediction ... See more keywords
Weak signals in high-dimension regression: detection, estimation and prediction.
Sign Up to like & getrecommendations! Published in 2019 at "Applied stochastic models in business and industry"
DOI: 10.1002/asmb.2340
Abstract: Regularization methods, including Lasso, group Lasso and SCAD, typically focus on selecting variables with strong effects while ignoring weak signals. This may result in biased prediction, especially when weak signals outnumber strong signals. This paper… read more here.
Keywords: estimation; variable selection; weak signals; estimation prediction ... See more keywords
Variable selection in competing risks models based on quantile regression.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8326
Abstract: The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence… read more here.
Keywords: selection competing; quantile regression; regression; competing risks ... See more keywords
SuRF: A new method for sparse variable selection, with application in microbiome data analysis.
Sign Up to like & getrecommendations! Published in 2020 at "Statistics in medicine"
DOI: 10.1002/sim.8809
Abstract: In this article, we present a new variable selection method for regression and classification purposes, particularly for microbiome analysis. Our method, called subsampling ranking forward selection (SuRF), is based on LASSO penalized regression, subsampling and… read more here.
Keywords: analysis; method; selection; variable selection ... See more keywords
Variable selection for censored data using Modified Correlation Adjusted coRrelation (MCAR) scores.
Sign Up to like & getrecommendations! Published in 2021 at "Statistics in medicine"
DOI: 10.1002/sim.9110
Abstract: Dealing with high-dimensional censored data is very challenging because of the complexities in data structure. This article focuses on developing a variable selection procedure for censored high-dimensional data with the AFT models using the Modified… read more here.
Keywords: mcar; mcar scores; correlation; censored data ... See more keywords
Controlled variable selection in Weibull mixture cure models for high‐dimensional data
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DOI: 10.1002/sim.9513
Abstract: Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought… read more here.
Keywords: mixture cure; cure; high dimensional; variable selection ... See more keywords
A systematic review and evaluation of statistical methods for group variable selection
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9620
Abstract: This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the… read more here.
Keywords: review; group; methods group; selection methods ... See more keywords
Genetic Algorithm‐Based Variable Selection in Prediction of Hot Metal Desulfurization Kinetics
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DOI: 10.1002/srin.201900090
Abstract: Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine‐grade desulfurization reagent. The selection of variables used for predicting the course… read more here.
Keywords: hot metal; metal; metal desulfurization; variable selection ... See more keywords
A simple method for forward variable selection and calibration: evaluation for compact and low-cost laser-induced breakdown spectroscopy system
Sign Up to like & getrecommendations! Published in 2017 at "Analytical and Bioanalytical Chemistry"
DOI: 10.1007/s00216-017-0247-4
Abstract: AbstractThis work presents a new method for forward variable selection and calibration and its evaluation for manganese determination in steel by laser-induced breakdown spectroscopy (LIBS). A compact and low-cost LIBS instrument was used, based on… read more here.
Keywords: forward variable; laser; method forward; variable selection ... See more keywords
Bootstrapping multiple linear regression after variable selection
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DOI: 10.1007/s00362-019-01108-9
Abstract: This paper suggests a method for bootstrapping the multiple linear regression model $$Y = \beta _1 + \beta _2 x_2 + \cdots + \beta _p x_p + e$$Y=β1+β2x2+⋯+βpxp+e after variable selection. We develop asymptotic theory… read more here.
Keywords: variable selection; multiple linear; linear regression; selection ... See more keywords
Variable selection of higher-order partially linear spatial autoregressive model with a diverging number of parameters
Sign Up to like & getrecommendations! Published in 2021 at "Statistical Papers"
DOI: 10.1007/s00362-021-01241-4
Abstract: In this paper, we consider problem of variable selection in higher-order partially linear spatial autoregressive model with a diverging number of parameters. By combining series approximation method, two-stage least squares method and a class of… read more here.
Keywords: proposed variable; variable selection; selection method; spatial autoregressive ... See more keywords