Results 91 to 100 of about 25,397 (329)
Difference based Ridge and Liu type Estimators in Semiparametric Regression Models [PDF]
We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε.
Esra Akdeniz Duran +2 more
core
Takeuchi's Information Criteria as a form of Regularization
, 2018 Takeuchi's Information Criteria (TIC) is a linearization of maximum likelihood estimator bias which shrinks the model parameters towards the maximum entropy distribution, even when the model is mis-specified.
Dixon, Matthew, Ward, Tyler
core +1 more source
Ridge, a computer program for calculating ridge regression estimates /
, 1977 Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.
Donald E. Hilt, Donald W. Seegrist
openaire +2 more sources
Advanced Science, EarlyView.This study reveals that maternal antibiotic exposure prior to conception disrupts intergenerational gut microbial succession. By enhancing maternal‐offspring microbial transmission, altering microbial developmental trajectories and increasing selective pressures during community assembly, these disturbances lead to persistent gut mucosal immaturity and
Yuzhu Chen +8 more
wiley +1 more source
A Note on The Moments of Stochastic Shrinkage Parameters in Ridge Regression [PDF]
A common problem in econometric models and multiple regression in general is multicollinearity, which produces undesirable effects on the Least Squares estimators.
Hernán Rubio, Luis Firinguetti
core
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates [PDF]
, 2014 We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge
Duchi, John C. +2 more
core
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
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
Advanced Science, EarlyView.A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source
Adaptive Monotone Shrinkage for Regression [PDF]
, 2015 We develop an adaptive monotone shrinkage estimator for regression models with the following characteristics: i) dense coefficients with small but important effects; ii) a priori ordering that indicates the probable predictive importance of the features.
Foster, Dean, Ma, Zhuang, Stine, Robert
core
Advanced Science, EarlyView.This study analyzes gut bacteria in cholangiocarcinoma patients, revealing distinct microbial signatures that enable accurate disease detection. Species‐based diagnostic models achieved over 98% accuracy in identifying cholangiocarcinoma and distinguished it from other liver diseases. The research demonstrates that specific beneficial bacteria suppress
Benchen Rao +18 more
wiley +1 more source