Results 11 to 20 of about 3,251,267 (306)
Bayesian Linear Regression [PDF]
, 2009 The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior).
Augustin, Thomas, Walter, Gero
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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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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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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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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.
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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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Local linear spatial quantile regression [PDF]
, 2009 Copyright @ 2009 International Statistical Institute / Bernoulli Society for Mathematical Statistics and Probability.Let {(Yi,Xi), i ∈ ZN} be a stationary real-valued (d + 1)-dimensional spatial processes.
Hallin, M, Lu, Z, Yu, K
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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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