Results 191 to 200 of about 6,156 (248)

Harnessing artificial intelligence for environmental sustainability via human capital and renewable energy. [PDF]

Sci Rep
Khan Z, Khan N, Zhu X.
europepmc   +1 more source

Joint Association of Household Pesticide Exposure With Depression in Adults: A Cross-Sectional Analysis From National Health and Nutrition Examination Survey 2007-2014. [PDF]

Depress Anxiety
Chen M, Dai H, Tong Z, Lin M.
europepmc   +1 more source

A novel hybrid interval prediction framework integrating multiobjective optimization and quantile deep learning for copper price prediction. [PDF]

Sci Rep
Wang Y, Du P, Xu Y, Wang J.
europepmc   +1 more source

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition. [PDF]

Am J Hum Genet
Legault MA, Hartford J, Arsenault BJ, Yang AY, Pineau J.   +4 more
europepmc   +1 more source

Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES. [PDF]

Medicine (Baltimore)
Wang Y, Deng M, Xu Y, Zha Z, Feng Y, Zhuang T, He J, Peng H, Wu W.   +8 more
europepmc   +1 more source

Causal K-Means Clustering. [PDF]

J R Stat Soc Series B Stat Methodol
Kim K, Kim J, Kennedy EH.
europepmc   +1 more source

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Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection

Information Sciences, 2021
Nonparametric quantile regression is a commonly used nonlinear quantile model. One general and popular approach is based on the use of kernels within a reproducing kernel Hilbert space (RKHS) framework, with the smoothing splines estimation as a special ...
Heng Lian, Fodé Zhang
exaly   +2 more sources

Nonparametric quantile scalar-on-image regression

Computational Statistics & Data Analysis
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chuchu Wang, Xinyuan Song
semanticscholar   +2 more sources

Quantile regression: a nonparametric approach

Computational Statistics and Data Analysis, 1988
Abstract Regression on any p -th quantile is considered through nonparametric modelling. The nonparametric technique used is moving parabolic fit which is known to be adaptative and to reduce bias in the usual mean regression. The quantile problem reduces to solving weighted linear regression in L 1 norm at each x -point and the iteratively ...
Pascal Sarda
exaly   +2 more sources

Convergence rate for nonparametric quantile regression with a total variation penalty

Stat, 2021
Quantile regression with a total variation penalty was previously proposed due to its computational expediency as well as its local adaptiveness. However, the convergence rate of the method in this setting has been not rigorously established.
Jiamin Liu, Heng Lian, Wangli Xu
exaly   +2 more sources