Results 11 to 20 of about 3,364,346 (276)
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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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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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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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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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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Partially linear censored quantile regression [PDF]
, 2009 Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate.
B Honore +17 more
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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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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
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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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