Sources of high leverage in linear regression model

06/07/2020
by   Myung Geun Kim, et al.
0

Some reasons for high leverage are analytically investigated by decomposing leverage into meaningful components. The results in this work can be used for remedial action as a next step of data analysis.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/11/2020

Bayesian Analysis on Limiting the Student-t Linear Regression Model

For the outlier problem in linear regression models, the Student-t linea...
09/02/2017

Adaptive Scaling

Preprocessing data is an important step before any data analysis. In thi...
07/11/2018

Proactive Intervention to Downtrend Employee Attrition using Artificial Intelligence Techniques

To predict the employee attrition beforehand and to enable management to...
10/21/2020

How Can Businesses Best Leverage Data scrubbing?

The hype around data is hardly news. It is a critical part of a business...
12/08/2017

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start

Bayesian optimization (BO) is a model-based approach for gradient-free b...
07/02/2020

Partial Trace Regression and Low-Rank Kraus Decomposition

The trace regression model, a direct extension of the well-studied linea...
10/18/2021

On completing a measurement model by symmetry

An appeal for symmetry is made to build established notions of specific ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

References

  • [1]
  • [2] [1.] S. Chatterji and A.S. Hadi, Sensitivity Analysis in Linear Regression, Wiley, New York, 1988.
  • [3]
  • [4] [2.] R.F. Gunst, Regression analysis with multicollinear predictor variables: definition, detection, and effects, Communications in Statistics: Theory and Methods, 12 (1983), 2217–2260.
  • [5]
  • [6] [3.] A.S. Hadi and P.F. Velleman, Comment on the paper by G.W. Stewart ”Collinearity and least squares regression”, Statistical Science, 2 (1987), 93–98.
  • [7]
  • [8] [4.] M.G. Kim, Case-deletion diagnostics for testing a linear hypothesis about regression coefficients, J. Appl. Math. & Computing, 10 (2002), 111–118.
  • [9]
  • [10] [5.] R.L. Mason and R.F. Gunst, Outlier-induced collinearities, Technometrics, 27 (1985), 401–407.
  • [11]
  • [12] [6.] P.J. Rousseeuw and A.M. Leroy,

    Robust Regression and Outlier Detection

    , Wiley, New York, 1987.
  • [13]
  • [14] [7.] G.W. Stewart, Collinearity and least squares regression (with discussions), Statistical Science, 2 (1987), 68–100.
  • [15]
  • [16]
  • [17] Department of Applied Statistics, Seowon University, 231 Mochung-Dong, Heungduk-Gu, Chongju, Chung-Buk, 361-742, Korea
  • [18]
  • [19] e-mail : mgkim@seowon.ac.kr
  • [20]