Results 11 to 20 of about 10,735,998 (316)
Truthful Linear Regression [PDF]
CoRR, 2015 We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy ...
Cummings, Rachel +2 more
core +7 more sources
CONTRASTIVE LINEAR REGRESSION. [PDF]
Ann Appl StatContrastive 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 +4 more
europepmc +5 more sources
Southwest Respiratory and Critical Care Chronicles, 2014 Gilbert Berdine, Shengping Yang
doaj +3 more sources
A mixture of linear-linear regression models for a linear-circular regression [PDF]
Statistical Modelling, 2019 We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modelling approach of a wrapped normal distribution that describes angular variables and angular distributions and advances them for a linear-circular regression analysis.
Sikaroudi, Ali Esmaieeli, Park, Chiwoo
openaire +3 more sources
Linearized binary regression [PDF]
2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018 Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields.
Andrew S. Lan +2 more
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Tensor Linear Regression: Degeneracy and Solution
IEEE Access, 2021 Tensor regression is an important and useful tool for analyzing multidimensional array data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed on the coefficient tensor parameter in the (penalized) loss ...
Ya Zhou, Raymond K. W. Wong, Kejun He
doaj +1 more source
A Review on Linear Regression Comprehensive in Machine Learning
, 2020 Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors.
Dastan Maulud, A. Abdulazeez
semanticscholar +1 more source
Secure Collaborative Computing for Linear Regression
Applied Sciences, 2023 Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute
Albert Guan, Chun-Hung Lin, Po-Wen Chi
doaj +1 more source
A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
Scientifica, 2020 The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models.
B. M. G. Kibria, A. Lukman, A. Lukman
semanticscholar +1 more source
Local linear spatial regression [PDF]
, 2004 A local linear kernel estimator of the regression function x\mapsto g(x):=E[Y_i|X_i=x], x\in R^d, of a stationary (d+1)-dimensional spatial process {(Y_i,X_i),i\in Z^N} observed over a rectangular domain of the form I_n:={i=(i_1,...,i_N)\in Z^N| 1\leq ...
Hallin, Marc, Lu, Zudi, Tran, Lanh T.
core +3 more sources