Results 131 to 140 of about 2,889,354 (160)

High-resolution photovoltaic power forecasting using machine learning models under seasonal and stress conditions. [PDF]

Sci Rep
Mansour HSE, Mohamed AS, Farh HMH, Ibrahim AW, Al-Shaalan AM, Kunya AB, Elnashar HS.   +6 more
europepmc   +1 more source

msBayesImpute as a versatile framework for addressing missing values in biomedical mass spectrometry proteomics data. [PDF]

Commun Chem
He J, Helm B, Gödtel F, Büchner K, Schilling M, Schneider MA, Klotz LV, Braunger J, Winter H, Velten B, Klingmüller U, Lu J.   +11 more
europepmc   +1 more source

GWAS-informed genomic selection for cold tolerance in pepper (Capsicum annuum L.). [PDF]

BMC Plant Biol
Lee K, Kim GW, Jeong HJ, Jeong HS, Kwon JK, Kang BC.   +5 more
europepmc   +1 more source

Growth Rate Variation in Brown Treesnakes (<i>Boiga irregularis</i>): An Invasive Species of Conservation Concern. [PDF]

Ecol Evol
Lardner B, Cade BS, Savidge JA, Rodda GH, Reed RN, Yackel Adams AA.   +5 more
europepmc   +1 more source

Volatile organic compounds exposure associated with frailty in United States adults from NHANES 2011-2018. [PDF]

Front Public Health
Qu TZ, Zhang T, Huang QY, Chen XL, Zhu Y.   +4 more
europepmc   +1 more source

Bayesian Adaptive Lasso Quantile Regression [PDF]

Statistical Modelling, 2012
Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective.
Keming Yu, Rahim Alhamzawi
exaly   +7 more sources

Bayesian Lasso Binary Quantile Regression [PDF]

Computational Statistics, 2013
In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers ...
Keming Yu, Rahim Alhamzawi
exaly   +4 more sources

Variational Inference on a Bayesian Adaptive Lasso Tobit Quantile Regression Model

Stat, 2023
The Tobit quantile regression model is a useful tool for quantifying the relationship between response variables with limited values and the explanatory variables. Under the Bayesian framework, the Tobit quantile regression model is often simulated by an asymmetric Laplacian distribution (ALD), which can be reformulated as a hierarchical structure ...
Weili Cheng
exaly   +3 more sources