A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation [PDF]
, 2020 Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables.
Cheong, Taesu +3 more
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Efficient semiparametric estimation of a partially linear quantile regression model [PDF]
, 2003 This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear. The paper develops a simple estimator of the parametric component of the conditional quantile.
Lee, S
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a multiple linear regression model [PDF]
, 2015 The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month.
Bissolli, Peter +3 more
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Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia's Hospital [PDF]
, 2015 Fuzzy linear regression analysis has become popular among researchers and standard model in analysing data vagueness phenomena. These models were represented by five statistical models such as multiple linear regression, fuzzy linear regression (Tanaka),
Ahmad Hilmi Azman +7 more
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, 2015 Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes.
Frank, Eibe +2 more
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Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model [PDF]
, 2013 This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model.
Kostov, Phillip
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Stein-Rule Estimation under an Extended Balanced Loss Function [PDF]
, 2007 This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model.
---, Shalabh +2 more
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Re-sampling in Linear Regression Model Using Jackknife and Bootstrap [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic.
Zakariya Y. Algamal, Khairy B. Rasheed
doaj +1 more source
On the First Order Regression Procedure of Estimation for Incomplete Regression Models [PDF]
, 1999 This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis ...
Srivastava, V. K., Toutenburg, Helge
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Different distance measures for fuzzy linear regression with Monte Carlo methods [PDF]
, 2016 The aim of this study was to determine the best distance measure for estimating the fuzzy linear regression model parameters with Monte Carlo (MC) methods.
Cattaneo, Marco E.G.V., İçen, Duygu
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