In chemometrical applications, covariates in regression models are often correlated, causing
a collinearity problem that can be solved by partial least squares (PLS) regression. In …

This work deals with log‐symmetric regression models, which are particularly useful when
the response variable is continuous, strictly positive, and following an asymmetric …

In this article, we propose a methodology that modifies the partial least squares (PLS)
regression algorithm. Certain steps of the algorithm are adjusted to address the estimation …

The partial least squares (PLS) regression is a statistical learning technique that solves
collinearity and/or high-dimensionality in the space of covariates. In this paper, we propose …

The drug design process has been evolving since its existence, and several knowledge
fields have contributed to the discovery and development of novel drugs. The emergence of …

Full article: A minimum matrix valued risk estimator combining restricted and ordinary least
squares estimators Skip to Main Content Taylor and Francis Online homepage Browse Search …

Biased estimation methods like ridge regression, Liu‐type regression, two‐parameter
regression and principal component regression have become very popular in the analysis of …

Wu and Yang [1] proposed an almost unbiased two-parameter estimator as an alternative to
the ordinary least squares estimator (OLSE) in a multicollinear regression model. We …

Partial least squares (PLS) models are a multivariate technique developed to solve the
problem of multicollinearity and/or high dimensionality related to explanatory variables in …