5 papers
Determining the Number of Components in PLS Regression on Incomplete Data
Titin Agustin Nengsih, Frédéric Bertrand, Myriam Maumy-Bertrand +1
Partial least squares regression---or PLS---is a multivariate method in which models are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively…
A random model for multidimensional fitting method
Hiba Alawieh, Frédéric Bertrand, Myriam Maumy-Bertrand +2
Multidimensional fitting (MDF) method is a multivariate data analysis method recently developed and based on the fitting of distances. Two matrices are available: one contains the…
Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data
Frédéric Bertrand, Philippe Bastien, Myriam Maumy-Bertrand
When cross-validating standard or extended Cox models, the commonly used criterion is the cross-validated partial loglikelihood using a naive or a van Houwelingen scheme -to make e…
plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R
F. Bertrand, M. Maumy-Bertrand
The aim of the plsRglm package is to deal with complete and incomplete datasets through several new techniques or, at least, some which were not yet implemented in R. Indeed, not o…
A Sheet of Maple to Compute Second-Order Edgeworth Expansions and Related Quantities of any Function of the Mean of an iid Sample of an Absolutely Continuous Distribution
F. Bertrand, M. Maumy-Bertrand
We designed a completely automated Maple () worksheet for deriving Edgeworth and Cornish-Fisher expansions as well as the acceleration constant of the bootstrap bias-…