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Books on the topic 'Partial least squares (pls) method'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Consult the top 15 books for your research on the topic 'Partial least squares (pls) method.'

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1

Hulland, John. Use of Partial Least Squares (PLS) in strategic management research. WesternBusiness School, University of Western Ontario, 1995.

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2

Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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3

D, Gunzburger Max, ed. Least-squares finite element methods. Springer, 2009.

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4

Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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5

Jiang, Bo-Nan. The least-squares finite element method: Theory and applications in computational fluid dynamics and electromagnetics. Springer, 1998.

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6

Jiang, Bo-nan. The Least-Squares Finite Element Method: Theory and Applications in Computational Fluid Dynamics and Electromagnetics. Springer Berlin Heidelberg, 1998.

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7

Banks, H. Thomas. Analytic semigroups: Applications to inverse problems for flexible structures. National Aeronautics and Space Administration, Langley Research Center, 1990.

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8

Saporta, Gilbert, Vincenzo Esposito Vinzi, Giorgio Russolillo, Laura Trinchera, and Hervé Abdi. The Multiple Facets of Partial Least Squares and Related Methods: PLS, Paris, France, 2014. Springer, 2016.

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9

Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Incorporated, 2013.

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10

Hair Jr., Joseph F., Christian M. Ringle, Siegfried P. Gudergan, Julen Castillo Apraiz, Gabriel A. Cepeda Carrión, and José Luis Roldán. Manual avanzado de Partial Least Squares Structural Equation Modeling (PLS-SEM). OmniaScience, 2021. http://dx.doi.org/10.3926/oss.407.

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Este libro es una traducción autorizada por Sage del libro Advanced Issues in Partial Least Squares Structural Equation Modeling. Concebido como una ampliación del Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (segunda edición)*, esta guía práctica de fácil manejo abarca contenido avanzado de PLS-SEM para ayudar a los estudiantes e investigadores a aplicar técnicas sobre problemas de investigación y a interpretar oportunamente los resultados. El libro aporta un resumen de conceptos básicos antes de centrarse en aspectos más avanzados. Además, ofrece amplios ejemplos del software SmartPLS 3 (www.smartpls.com) y viene acompañado de bases de datos de descarga gratuita. En el libro se subraya la necesidad de aplicar cuidadosamente cualquier enfoque de PLS-SEM para asegurarnos de que éste encaje con el contexto de investigación y las características de los datos. Características clave: · Uso limitado de fórmulas, ecuaciones, símbolos y notaciones similares que hace que se comprendan los conceptos fácilmente. · Disponibilidad de reglas prácticas en cada capítulo que aportan guías concretas de mejores prácticas para aplicar e interpretar PLS-SEM y preparar artículos para su publicación en revistas científicas. · Empleo de un mismo ejemplo y base de datos a lo largo de todo el libro para facilitar la consistencia en los aspectos relacionados con el caso propuesto. · Conjunto de recursos que se pueden descargar de manera gratuita: bases de datos y proyectos de SmartPLS para practicar las técnicas planteadas. · Manual traducido y actualizado con pies de página con los desarrollos más recientes de PLS-SEM. -------- *Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (segunda edición) Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrión, G., & Roldán, J. L. (2019). Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM). OmniaScience Scholar, Terrassa, Barcelona.
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11

A primer on partial least squares structural equation modeling (PLS-SEM) - 2. edición. SAGE Publications, 2017.

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12

Hair Jr., Joseph F., G. Tomas M. Hult, Christian M. Ringle, et al. Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda Edición). OmniaScience, 2019. http://dx.doi.org/10.3926/oss.37.

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13

Gunzburger, Max D., and Pavel B. Bochev. Least-Squares Finite Element Methods. Springer, 2011.

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14

A, Rebnord D., and Langley Research Center, eds. Analytic semigroups: Applications to inverse problems for flexible structures. National Aeronautics and Space Administration, Langley Research Center, 1990.

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15

Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.
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