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Partially Generalized Least Squares and Two-Stage Least Squares Estimators
Journal of Econometrics, 1983Abstract A class of partially generalized least squares estimators and a class of partially generalized two-stage least squares estimators in regression models with heteroscedastic errors are proposed. By using these estimators a researcher can attain higher efficiency than that attained by the least squares or the two-stage least squares estimators ...
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Partial Least Squares Image Clustering
2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one against-all Partial Least Squares classifier to find image ...
Ricardo Barbosa Kloss +4 more
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Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and ...
Aniruddha Kembhavi +2 more
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Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS).
Hiroto Saigo +2 more
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Partial Least Squares Path Modeling
2017Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions.
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A Probabilistic Derivation of the Partial Least-Squares Algorithm
Journal of Chemical Information and Computer Sciences, 2001Traditionally the partial least-squares (PLS) algorithm, commonly used in chemistry for ill-conditioned multivariate linear regression, has been derived (motivated) and presented in terms of data matrices. In this work the PLS algorithm is derived probabilistically in terms of stochastic variables where sample estimates calculated using data matrices ...
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A Reformulation of the Partial Least Squares Regression Algorithm
SIAM Journal on Scientific Computing, 1994Let \(X = (x_ 1,\dots,x_ k)\), where \(x_ 1,\dots,x_ k\) are \(n\)- dimensional vectors (independent variables). Also available is an associated \(n\)-dimensional vector \(y\) (dependent variable). One of the main aims of linear regression is to predict the values of the dependent variable using a linear combination of the independent variables ...
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2015
Partial least squares (PLS) path modeling is a variance-based form of structural equation modeling. It is frequently applied in business and social sciences to analyze complex causal-predictive models involving latent variables. PLS path modeling makes only soft assumptions with respect to the data distribution, and is relatively robust in case of ...
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Partial least squares (PLS) path modeling is a variance-based form of structural equation modeling. It is frequently applied in business and social sciences to analyze complex causal-predictive models involving latent variables. PLS path modeling makes only soft assumptions with respect to the data distribution, and is relatively robust in case of ...
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Implementing partial least squares
Statistics and Computing, 1995Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent.
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