Results 41 to 50 of about 177,424 (286)
Random design analysis of ridge regression
, 2014 This work gives a simultaneous analysis of both the ordinary least squares estimator and the ridge regression estimator in the random design setting under mild assumptions on the covariate/response distributions.
Hsu, Daniel +2 more
core +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Scientific African, 2022 The parameters in the Poisson regression model are usually estimated using the maximum likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or outliers in the Poisson regression model.
Kingsley C Arum +2 more
doaj +1 more source
Efficiency of conformalized ridge regression [PDF]
, 2014 Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions ...
Burnaev, Evgeny, Vovk, Vladimir
core
Advanced Engineering Materials, EarlyView.Laser surface texturing significantly improves the corrosion resistance and mechanical strength of 3D‐printed iron polylactic acid (Ir‐PLA) for marine applications. Optimal laser parameters reduce corrosion by 80% and enhance tensile strength by 25% and ductility by 15%.
Mohammad Rezayat +6 more
wiley +1 more source
Weighted Ridge Regression: Combining Ridge and Robust Regression Methods [PDF]
, 1973 This paper gives the formulas for and derivation of ridge regression methods when there are weights associated with each observation. A Bayesian motivation is used and various choices of k are discussed. A suggestion is made as to how to combine ridge regression with robust regression methods.
openaire +2 more sources
Local earthquake magnitude estimation using ridge regression model
Electronics Letters, 2022 In this paper, a local magnitude estimation model using ridge regression is proposed for the accurate determination of the local magnitude of earthquakes.
Hyeongki Ahn +4 more
doaj +1 more source
Ridge Regression, Hubness, and Zero-Shot Learning
, 2015 This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space.
Hara, Kazuo +4 more
core +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Recovering Jackknife Ridge Regression Estimates from OLS Results [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2014 The aim of this paper is addressing or recalculate the estimation methods in multiple linear regression model when there is a problem of Multicollinearity in this model like the ridge regression for Hoerl and Kannard, Baldwin estimator (HKB) and ...
Feras Sh. Mahmood
doaj +1 more source