PCovR2: A flexible principal covariates regression approach to parsimoniously handle multiple criterion variables

A pseudo-Bayesian shrinkage approach to regression with missing covariates.

We consider the linear regression of outcome Y on regressors W and Z with some values of W missing, when our main interest is the effect of Z on Y, controlling for W. Three common approaches to regression with missing covariates are (i) complete-case analysis (CC), which discards the incomplete cases, and (ii) ignorable likelihood methods, which base inference on the likelihood based on the obs...

A principal component approach to dynamic regression models

In this paper we introduce a dynamic regression model that states how an output is related to an input allowing future values forecasting. The basic tools to set up this model are the orthogonal decomposition of a discrete time stochastic process by means of its principal components analysis, and the linear regression performed on the principal components of input and output processes. The beha...

Regression with imputed covariates: A generalized missing-indicator approach

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this tradeof...

a segmented principal component analysis-regression approach to qsar study of peptides

A flexible regression approach using GAMLSS