Results 11 to 20 of about 1,688,491 (311)
enphysoft/inverse-linear-regression:
, 2021 This site includes MATLAB source codes, used to generate paired data sets, studied at Albert S. Kim, Inverse Sampling of Degenerate Datasets from a Linear Regression Line, arXiv:2108.11477 [stat.ME], https://arxiv.org/abs/2108 ...
Albert S. Kim
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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
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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
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Linear expectile regression under massive data
Fundamental Research, 2021 In this paper, we study the large-scale inference for a linear expectile regression model. To mitigate the computational challenges in the classical asymmetric least squares (ALS) estimation under massive data, we propose a communication-efficient divide
Shanshan Song, Yuanyuan Lin, Yong Zhou
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Validating linear restrictions in linear regression models with general error structure [PDF]
, 2006 A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability.
Holzmann, Hajo +2 more
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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
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Shrinkage Estimators for the Intercept in Linear and Uplift Regression
Scientific Annals of Computer Science, 2023 Shrinkage estimators modify classical statistical estimators by scaling them towards zero in order to decrease their prediction error. We propose shrinkage estimators for linear regression models which explicitly take into account the presence ...
Szymon Jaroszewicz, Krzysztof Rudas
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Linear regression in genetic association studies. [PDF]
PLoS ONE, 2013 In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype
Petra Bůžková
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MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA
Barekeng, 2023 When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses
Eni Meylisah +4 more
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Finite Mixtures of Generalized Linear Regression Models [PDF]
, 2007 Generalized linear models have become a standard technique in the statistical modelling toolbox for investigating relationships between variables.
Bettina Grün +3 more
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