Results 101 to 110 of about 2,329,699 (248)
Attention-Based Quantile Regression for RUL Uncertainty Prediction
SensorsRemaining Useful Life prediction is a core challenge in the field of Prognostics and Health Management. Traditional point prediction methods only provide a single estimate and cannot quantify prediction uncertainty, limiting their application in critical
Lin Huang +4 more
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Journal of the Operations Research Society of Japan, 1991 Summary: This study proposes a new use of goal programming for empirically estimating a regression quantile hyperplane. The approach can yield regression quantile estimates that are less sensitive to not only non- Gaussian error distributions but also a small sample size than conventional regression quantile methods.
openaire +2 more sources
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
Corrigendum: Modified quantile regression for modeling the low birth weight
Frontiers in Applied Mathematics and Statistics, 2023 Ferra Yanuar +2 more
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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
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Interpretable Short‐Term Electric Load Forecasting
Advanced Intelligent Systems, EarlyView.A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola +6 more
wiley +1 more source
M-quantile regression analysis of temporal gene expression data [PDF]
, 2009 In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions.
Vinciotti, V, Yu, K
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Quantile Regression with Classical Additive Measurement Errors [PDF]
This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models.
Gabriel Montes-Rojas
core
Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science
Advanced Intelligent Systems, EarlyView.To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska +7 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
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