Results 121 to 130 of about 2,542,650 (299)
Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification
Econometrics, 2015 Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β (
Ying-Ying Lee
doaj +1 more source
AIChE Journal, EarlyView.Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin +2 more
wiley +1 more source
REGRESI KUANTIL MEDIAN UNTUK MENGATASI HETEROSKEDASTISITAS PADA ANALISIS REGRESI
E-Jurnal Matematika, 2013 In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best,
IDA AYU PRASETYA UTHAMI +2 more
doaj +1 more source
Extremal quantile regression: an overview
, 2017 32 pages, 4 tables, 7 figures, forthcoming in the Handbook of Quantile ...
Chernozhukov, Victor +2 more
openaire +3 more sources
Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns
Advanced Intelligent Discovery, EarlyView.Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer +6 more
wiley +1 more source
Quantile Regression in Risk Calibration [PDF]
Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the ...
Wolfgang Karl Härdle +2 more
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Advanced Intelligent Discovery, EarlyView.Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang +14 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2019 Introduction Normative cognitive data can help to distinguish pathological decline from normal aging. This study presents normative data from the Cambridge Neuropsychological Test Automated Battery, using linear regression and nonlinear quantile ...
Rosemary A. Abbott +12 more
doaj +1 more source
The Immigrant-Native Born Earnings Gap in the US: a Quantile Regression Analysis and International Comparison [PDF]
Immigrants, Earnings Distributions, Rates of Return, Quantile ...
Anh Tram Le +2 more
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