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Adiabatic quantum linear regression [PDF]

Scientific Reports, 2021
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on ...
Prasanna Date, Thomas Potok
doaj   +6 more sources

CONTRASTIVE LINEAR REGRESSION. [PDF]

Ann Appl Stat
Contrastive dimension reduction methods have been developed for case-control study data to identify variation that is enriched in the foreground (case) data X relative to the background (control) data Y. Here, we develop contrastive regression for the setting when there is a response variable r associated with each foreground observation.
Zhang B, Nyquist S, Jones A, Engelhardt BE, Li D.   +4 more
europepmc   +5 more sources

Benign overfitting in linear regression [PDF]

Proceedings of the National Academy of Sciences, 2019
The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodology: deep neural networks seem to predict well, even with a perfect fit to noisy training data.
P. Bartlett, Philip M. Long, G. Lugosi, Alexander Tsigler   +3 more
semanticscholar   +8 more sources

Linear regression analysis study

Journal of the Practice of Cardiovascular Sciences, 2018
Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables.
Khushbu Kumari, Suniti Yadav
doaj   +2 more sources

Application and interpretation of linear-regression analysis. [PDF]

Med Hypothesis Discov Innov Ophthalmol
Background: Linear-regression analysis is a well-known statistical technique that serves as a basis for understanding the relationships between variables.
Roustaei N.
europepmc   +2 more sources

Linear Regression

Southwest Respiratory and Critical Care Chronicles, 2014
Gilbert Berdine, Shengping Yang
doaj   +2 more sources

Regression Analysis with Scikit-Learn (part 1 - Linear)

The Programming Historian, 2022
This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn).
Matthew J. Lavin
doaj   +1 more source

Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality [PDF]

Journal of The Royal Statistical Society Series B-statistical Methodology, 2020
This paper considers estimation and prediction of a high‐dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models ...
Sai Li, T. Cai, Hongzhe Li
semanticscholar   +1 more source

Function-on-function linear quantile regression

Mathematical Modelling and Analysis, 2022
In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
doaj   +1 more source

Post-processing through linear regression [PDF]

Nonlinear Processes in Geophysics, 2011
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed,
B. Van Schaeybroeck, S. Vannitsem
doaj   +1 more source