Results 21 to 30 of about 8,779,194 (300)
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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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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Robust Model Selection in Linear regression [PDF]
Kirkuk Journal of Science, 2007 The research deals with the proposing of robust formula for the accumulate prediction error (APE) criterion which is used in selecting regression model. The proposed formula evaluated with a simulation study.
Dr. Sabah Haseeb Hassan
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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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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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European Journal of Operational Research, 2021 In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models.
Elena Dumitrescu +3 more
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Research on linear regression algorithm [PDF]
MATEC Web of ConferencesLinear regression is one of the most widely used predictive models in statistics and machine learning. This paper aims to comprehensively discuss the theoretical basis, mathematical principle and application of linear regression algorithm in various ...
Qu Kecheng
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Bayesian linear regression for surface roughness prediction
, 2020 To improve the prediction accuracy of surface roughness in milling process, this paper provides an unique feature extraction method and comprehensively analyzes four types of Bayesian linear regression (BLR) model (Standard_BLR, Gaussian_BLR ...
Dongdong Kong +4 more
semanticscholar +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Previous work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. By contrast, we may be interested in finding a segment of the population for which we can find a linear rule capable of achieving more accurate ...
Diego Calderon +3 more
openaire +5 more sources