Results 31 to 40 of about 2,939,863 (258)

Improved Redundant Rule-Based Stochastic Gradient Algorithm for Time-Delayed Models Using Lasso Regression

open access: yesIEEE Access, 2022
This paper proposes an improved redundant rule based lasso regression stochastic gradient (RR-LR-SG) algorithm for time-delayed models. The improved SG algorithm can update the parameter elements with different step-sizes and directions, thus it is more ...
Hangtao Zhao, Lixin Lv, Yuejiang Ji
doaj   +1 more source

Competence region estimation for black-box surrogate models

open access: yesProceedings of the International Florida Artificial Intelligence Research Society Conference, 2021
With advances in edge applications for industry andhealthcare, machine learning models are increasinglytrained on the edge. However, storage and memory in-frastructure at the edge are often primitive, due to costand real-estate constraints.
Tapan Shah
doaj   +1 more source

Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors [PDF]

open access: yesJournal of Sciences, Islamic Republic of Iran, 2019
In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression ...
Hoda Mohammadi, Abdolrahman Rasekh
doaj   +1 more source

Design and analysis strategies for robust microbiome ageing research

open access: yesFEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik   +5 more
wiley   +1 more source

Semiparametric linear regression with censored data and stochastic regressors [PDF]

open access: yes, 1994
We propose three new estimation procedures in the linear regression model with randomly-right censored data when the distribution function of the error term is unspecified, regressors are stochastic and the distribution function of the censoring variable
Mora, Juan
core   +1 more source

Discerning protein pools by selective staining with self‐labeling tags

open access: yesFEBS Letters, EarlyView.
Cell surface proteins have an intra‐ and extracellular pool. Combining genetic fusion to self‐labeling tags that can be addressed with small molecule fluorophores allows separating these pools. We highlight recent developments and techniques for state‐of‐the‐art interrogation of cell surface proteins in the complex tissue setting.
Kati Fischermanns, Johannes Broichhagen
wiley   +1 more source

On the Stochastic Restricted r-k Class Estimator and Stochastic Restricted r-d Class Estimator in Linear Regression Model

open access: yesJournal of Applied Mathematics, 2014
The stochastic restricted r-k class estimator and stochastic restricted r-d class estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. The mean squared error matrix of the proposed
Jibo Wu
doaj   +1 more source

Accounting for measurement error in log regression models with applications to accelerated testing. [PDF]

open access: yesPLoS ONE, 2018
In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals.
Robert Richardson   +3 more
doaj   +1 more source

Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry

open access: yesFEBS Letters, EarlyView.
EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri   +5 more
wiley   +1 more source

Online Quantum Mixture Regression for Trajectory Learning by Demonstration [PDF]

open access: yes, 2013
16/01/14 MEB. Pre-print version OK to add.In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states,
Dimitrios Korkinof   +3 more
core   +1 more source

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