Results 261 to 270 of about 460,060 (291)

A Procedure for Robust Estimation and Inference in Linear Regression

1991
Even if robust regression estimators have been around for nearly 20 years, they have not found widespread application. One obstacle is the diversity of estimator types and the necessary choices of tuning constants, combined with a lack of guidance for these decisions.
Victor J. Yohai, Werner A. Stahel, Ruben H. Zamar   +2 more
openaire   +1 more source

Robust likelihood inference for regression parameters in partially linear models

Computational Statistics & Data Analysis, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chung-Wei Shen, Tsung-Shan Tsou, N. Balakrishnan 0002   +2 more
openaire   +1 more source

Linear Regression: Inference

2022
openaire   +1 more source

A study over the general formula of regression sum of squares in multiple linear regression

Numerical Methods for Partial Differential Equations, 2021
Mehmet Korkmaz
exaly  

Inference for linear regression models in the presence of heteroskedasticity

2017
The linear regression model is a highly versatile tool with which one can model a response variable in terms of one or more predictor variables. The classical linear model is based on six primary theoretical assumptions. In this study the main focus is on the assumption of "homoskedasticity" and the violation thereof, called "heteroskedasticity ...
openaire   +2 more sources

Bootstrap inference in functional linear regression models

We consider functional linear regression models (FLRMs) with functional regressor and scalar response, where the inference of the slope function is an important problem. However, even though asymptotic inference methods exist in FLRMs, these methods are limited in applicability because a wrong scaling factor is used; truncation bias in the limit is ...
openaire   +2 more sources

Inference on linear quantile regression with dyadic data

Journal of Statistical Planning and Inference
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The number of subjects per variable required in linear regression analyses

Journal of Clinical Epidemiology, 2015
Peter Austin, Ewout Steyerberg
exaly  

Discriminative Elastic-Net Regularized Linear Regression

IEEE Transactions on Image Processing, 2017
Zheng Zhang, Zhihui Lai, Yong Xu
exaly  

A comparison of random forest regression and multiple linear regression for prediction in neuroscience

Journal of Neuroscience Methods, 2013
Paul F Smith, Siva Ganesh, Ping Liu
exaly