Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years. In this letter, we propose a Bayesian formulation of PLS along with s...

Extracting facial feature points such as eyes, mouth and nose plays an important role in many applications. Most of the proposed methods are base on the geometrical features of images. In this paper, a novel method based on Partial Least Square Regression (PLSR) model is introduced to extract the relationship between the feature point coordinates and gray value distribution in the image. The pr...

Some of the models using partial least squares (PLS) in Information Systems (IS) field may have serious problems because do not properly address endogeneity. This may suppose a problem in IS theory building because it may lead IS scholars to non-correct results. Although the IS community’s awareness is rising, we do not have a clear understanding of the problem nor fine-grained practical guidel...

This research examines the time management strategies of individuals and gathers information on the complex temporal structures they experience and manage. Its focus is on understanding the relationship between the quality of individual time management and an individual’s understanding and use of temporal structures involving electronic calendar systems. This work consists of a survey study whi...

Approaches for meaningful regressor construction in the linear prediction problem are investigated in a framework similar to partial least squares and continuum regression, but weighted to allow for intelligent specification of an evaluative scheme. A cross-validatory continuum regression procedure is proposed, and shown to compare well with ordinary continuum regression in empirical demonstrat...

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4 We build connections between envelopes, a recently proposed context for efficient estima5 tion in multivariate statistics, and multivariate partial least squares (PLS) regression. In partic6 ular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which 7 opens the door to pursuing the same goals as PLS but using different envelope estimators. It 8 is argued that...

A recent SMJ article (Hulland, 1999) raises interesting questions about the proper use of partial least squares (PLS) in strategy research. The present note examines some of the issues raised in the article, and briefly extends the methodological discussion to the broader aspects of theory and empirical research in strategy.