Multicollinearity in Path Analysis: A Simple Method to Reduce Its Effects

By Tiago Olivoto, V. Souza, M. Nardino, I. Carvalho, M. Ferrari, A. J. D. Pelegrin, V. J. Szareski, Denise Schmidt. 2017

63 citations recorded by the source corpus.

Subfields: Genetics, Plant Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e8813350e23e088aa077b24358a79227e5dbc2a0

How researchers see this paper

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

In Genetics

1. Tiago Olivoto
2. M. Nardino

In Plant Science

1. Tiago Olivoto
2. M. Nardino

In Statistics and Probability

1. Tiago Olivoto
2. M. Nardino

Citations in the corpus

• MGIDI: towards an effective multivariate selection in biological experiments (2020, background): “Consequently, the multicollinearity issue (Graham, 2003; Olivoto et al., 2017), is an obstacle faced by the SH index.”