Results 131 to 140 of about 52,512 (299)
Ridge regression : biased estimation based on ill-conditioned data
, 1979 Multiple linear regression is a widely used statistical method. Its application, especially in the sciences, social sciences, and economics assists administrators in evaluating programs and planners in predicting future situations.
Bulmahn, Barbara J.
core
Beyond Ridge Regression: Enhancing Distribution of Relaxation Times Deconvolution
The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression ...
Francesco, Ciucci, Baptiste, Py
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Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
OKRidge: Scalable Optimal k-Sparse Ridge Regression. [PDF]
Adv Neural Inf Process Syst, 2023 Liu J, Rosen S, Zhong C, Rudin C.
europepmc +1 more source
A ridge regression method for improving the semiparametric regression with sparse data
, 2015 碩士在無母數迴歸分析中，區域線性估計量同時具有較簡潔的漸進偏誤和較小的漸進變異數；然而，Seifert與Gasser(1996) 指出在有限樣本下，當資料稀疏或取樣點彼此很靠近時，區域線性估計量的條件變異數並沒有上界，估計出來的曲線因而崎嶇不平；為了改善這個問題，Seifert與Gasser(1996)結合了區域線性平滑法和脊迴歸的概念，建構出區域線性脊迴歸估計量。 本篇論文使用Seifert與Gasser (1996)的區域線性脊迴歸方法來估計同時具有多重線性迴歸式與無母數迴歸函數的半參數迴歸 ...
吳佳翰; Wu, Jia-Han
core
fracridge: fractional ridge regression
, 2020 Ridge regression is a key regularization technique that penalizes the L2-norm of the coefficient values in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter alpha that controls the amount of ...
Kay, Kendrick, Rokem, Ariel
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AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Improved estimation of earth rotation parameters using the adaptive ridge regression
, 1998 The multicollinearity among regression variables is a common phenomenon in the reduction of astronomical data. The phenomenon of multicollinearity and the diagnostic factors are introduced first.
Huang, CL, Jin, WJ
core
, 2014 Permutation tests for equality of the group distributions using distance components analysis (lines 2 to 6), and permutation F-tests for the presence of 2-by-2 interactions (lines 7 to 16), in the comparison of ridge-regression vs lasso. Results based on
Elias Chaibub Neto (42416) +2 more
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Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source