Results 11 to 20 of about 8,779,194 (300)
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.
Lan, Andrew S. +2 more
openaire +3 more sources
Knowledge and Awareness: Linear Regression [PDF]
Educational Process: International Journal, 2016 Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task.
Monika Raghuvanshi
doaj +1 more source
Differentially Private Simple Linear Regression [PDF]
Proceedings on Privacy Enhancing Technologies, 2020 Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data.
Daniel Alabi +4 more
semanticscholar +1 more source
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
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 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
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
Psychiatry and Behavioral Sciences, 2013 Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression.
Selim Kılıc
doaj +1 more source
Diabetes & metabolic syndrome, 2020 Introduction and Aims The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world.
Smita Rath +2 more
semanticscholar +1 more source