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Books on the topic 'Partial least square analysis'

Author: Grafiati

Published: 19 February 2023

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1

Lohmöller, Jan-Bernd. Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag, 1989.

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2

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

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3

Xie, Bei, and Tamal Bose. Partial Update Least-Square Adaptive Filtering. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01681-3.

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4

Harmonic maps and differential geometry: A harmonic map fest in honour of John C. Wood's 60th birthday, September 7-10, 2009, Cagliari, Italy. Providence, R.I: American Mathematical Society, 2011.

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5

J, Dunn W., Scott D. R. 1934-, and United States. Environmental Protection Agency., eds. Principal components analysis and partial least squares regression. [Washington, D.C.?: U.S. Environmental Protection Agency, 1992.

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6

Lohmöller, Jan-Bernd. Latent Variable Path Modeling with Partial Least Squares. Physica-Verlag, 2013.

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7

Avkiran, Necmi K., and Christian M. Ringle. Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance. Springer, 2019.

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8

Avkiran, Necmi K., and Christian M. Ringle. Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance. Springer, 2018.

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9

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

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10

Handbook of Partial Least Squares: Concepts, Methods and Applications. Springer, 2016.

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11

Handbook of Partial Least Squares: Concepts, Methods and Applications. Springer, 2010.

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12

Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Incorporated, 2021.

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13

Hult, G. Tomas M., Joseph F. Jr Hair, Marko Sarstedt, and Christian M. Ringle. Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Incorporated, 2013.

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14

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

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15

Hult, G. Tomas M., Joseph F. Jr Hair, Marko Sarstedt, and Christian M. Ringle. Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Incorporated, 2016.

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16

(Editor), Vincenzo Esposito Vinzi, Wynne W. Chin (Editor), Joerg Henseler (Editor), and Huiwen Wang (Editor), eds. Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields (Springer Handbooks of Computational Statistics). Springer, 2008.

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17

Rouleau, Jessica Leigh. Partial least squares modeling of keratinocyte differentiation through the integration of gene expression profiles and flow cytometry analysis. 2007.

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18

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

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19

Bose, Tamal, and Bei Xie. Partial Update Least-Square Adaptive. Morgan & Claypool Publishers, 2014.

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20

Bose, Tamal, and Bei Xie. Partial Update Least-Square Adaptive Filtering. Morgan & Claypool Publishers, 2014.

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21

Bose, Tamal, and Bei Xie. Partial Update Least-Square Adaptive Filtering. Springer International Publishing AG, 2014.

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22

Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.

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This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those familiar with the material. The presentation of one- and two-parameter chi-squared model testing, requiring only elementary probability and algebra, is followed by case studies that apply the methods to simple introductory physics lab experiments. More challenging topics, requiring calculus, are addressed in an advanced topics chapter. This self-contained and student-friendly introduction to chi-squared analysis and model testing includes a glossary, end-of-chapter problems with complete solutions, and software scripts written in several popular programming languages, that the reader can use for chi-squared model testing. In addition to introductory physics lab students, this accessible introduction to chi-squared analysis and model testing will be of interest to all who need to learn chi-squared model testing, e.g. beginning researchers in astrophysics and particle physics, beginners in data science, and lab students in other experimental sciences.

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23

United States. National Aeronautics and Space Administration., ed. Subspace based signal analysis of partially polarized light reflected by plant canopies. [Washington, DC: National Aeronautics and Space Administration, 1996.

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24

Ahmed, Mahbub I. Performance analysis of least square error [omega] filter for image reconstruction from projection. 1990.

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25

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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26

O. Görgülü and A. Akilli. Egg production curve fitting using least square support vector machines and nonlinear regression analysis. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.235.

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27

Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.

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28

Hwui, Chan Sane, and Lay Yoon Fah. Affective Domains Contributing to Behavioural Intention in Teaching Science. UMS Press, 2020. http://dx.doi.org/10.51200/affectivedomainsumspress2020-978-967-2962--27-4.

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The teaching profession is a highly stressful occupation and susceptible to burnout due to high levels of workload compared to other contact occupations. In Malaysia, the majority of science teachers are experiencing physical and mental drain after long periods of teaching service and mundane routine year in and year out. Despite the general assumptions of a teacher’s job is merely teaching from a textbook, a teacher’s workload includes teaching-related (class preparation and classroom management) and non-teaching related (administration and meetings). All these challenges required teachers to possess high self-efficacy beliefs, great teaching motivation, and positive attitudes toward teaching science. When the development of the affective domain is neglected during pre-service years, teachers’ behavioural intention in teaching science will subside gradually. This may cause emotional exhaustion, feelings of ineffectiveness, and job burnout when the pre-service teachers start to work in a high-pressure environment. The prime focus of this book includes the use of Partial Least Square-Structural Equation Modelling (PLS-SEM) approach in studying the mediating effect of attitudes toward teaching science in the relationship between teacher self-efficacy beliefs and teaching motivation on behavioural intention in teaching science. This book provides insights for policymakers to formulate courses on managing personal affective domains in the teacher education curriculum. It is also hoped that this book will be of interest to academicians and researchers on the topic related to teacher education and teaching professional development.

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