Robust Analysis of Linear Models

By Joseph W. McKean. 2004

83 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76caff6e696e90ef00fd3854ab509324b547c81b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. C. Zygmont
2. Mario R. Smith
3. Brent A. Johnson

In Applied Mathematics

1. C. Zygmont
2. Mario R. Smith
3. Brent A. Johnson

In Statistics and Probability

1. C. Zygmont
2. Mario R. Smith
3. Brent A. Johnson

Citations in the corpus

• Variable selection in semiparametric linear regression with censored data (2008, background): “In penalized least squares regression with L1 penalty, however, some related work has been completed; notable papers include Huang (2003), Rosset and Zhu (2004). These papers argue that, with probability tending to one, there exists a range of regularization parameters λ such that the average square”
• Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, background): “McKean (2004), and Terpstra and McKean (2005), suggest R routines for the weighted Wilcoxon techniques (WW) providing a useful option for testing linear models when normality assumptions are violated or there are outliers in both the x-and y-spaces.”