Results 41 to 50 of about 10,735,998 (316)
Linear Regression for Heavy Tails
Risks, 2018 There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares.
Guus Balkema, Paul Embrechts
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Kernel Logistic Regression-linear for Leukemia Classification Using High Dimensional Data [PDF]
, 2009 Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques.
Embong, A. (A) +3 more
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Transportes, 2020 Estimating the number of trips generated by a company is an essential part of the process of freight demand modelling. In this context, the current study examines freight trip generation to buildings under construction (BUC) using generalised linear ...
Leise Kelli de Oliveira +3 more
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Using Simulation In Teaching Simple Linear Regression [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2012 This paper aims to use simulation in teaching simple linear regression, computer stimulation can be used to teach complicated statistical concepts in linear regression more quickly and effectively than traditional lecture alone.
ALBRIA A., ISAM K. ALANI, AHMED H. ALANI
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Information-Based Optimal Subdata Selection for Big Data Linear Regression [PDF]
Journal of the American Statistical Association, 2017 Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large datasets due to computational limitations.
Haiying Wang, Min Yang, J. Stufken
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Distributed Online Linear Regressions
IEEE Transactions on Information Theory, 2021 We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the objective of fitting the \emph{network-wide} data. It then updates its predictor for the next round according to the received local feedback and information received from ...
Deming Yuan +2 more
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Sparse Semi-Functional Partial Linear Single-Index Regression
Proceedings, 2018 The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task.
Silvia Novo +2 more
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Scaled Sparse Linear Regression
, 2012 Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling ...
Sun, Tingni, Zhang, Cun-Hui
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Linear regression and the normality assumption.
Journal of Clinical Epidemiology, 2017 OBJECTIVES Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and
A. Schmidt, C. Finan
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Clustered linear regression [PDF]
Knowledge-Based Systems, 2002 Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values.
Ari, B., Güvenir H.A.
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