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Journal articles on the topic 'Partial least squares analysis'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Liland, Kristian Hovde, and Ulf Geir Indahl. "Powered partial least squares discriminant analysis." Journal of Chemometrics 23, no. 1 (January 2009): 7–18. http://dx.doi.org/10.1002/cem.1186.

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2

Ketterlinus, Robert D., Fred L. Bookstein, Paul D. Sampson, and Michael E. Lamb. "Partial least squares analysis in developmental psychopathology." Development and Psychopathology 1, no. 4 (October 1989): 351–71. http://dx.doi.org/10.1017/s0954579400000523.

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AbstractDespite extensive theoretical and empirical advances in the last two decades, little attention has been paid to the development of statistical techniques suited for the analysis of data gathered in studies of developmental psychopathology. As in most other studies of developmental processes, research in this area often involves complex constructs, such as intelligence and antisocial behavior, measured indirectly using multiple observed indicators. Relations between pairs of such constructs are sometimes reported in terms of latent variables (LVs): linear combinations of the indicators of each construct. We introduce the assumptions and procedures associated with one method for exploring these relations: partial least squares (PLS) analysis, which maximizes covariances between predictor and outcome LVs; its coefficients are correlations between observed variables and LVs, and its LVs are sums of observable variables weighted by these correlations. In the least squares logic of PLS, familiar notions about simple regressions and principal component analyses may be reinterpreted as rules for including or excluding particular blocks in a model and for “splitting” blocks into multiple dimensions. Guidelines for conducting PLS analyses and interpreting their results are provided using data from the Goteborg Daycare Study and the Seattle Longitudinal Prospective Study on Alcohol and Pregnancy. The major advantages of PLS analysis are that it (1) concisely summarizes the intercorrelations among a large number of variables regardless of sample size, (2) yields coefficients that are readily interpretable, and (3) provides straightforward decision rules about modeling. The advantages make PLS a highly desirable technique for use in longitudinal research on developmental psychopathology. The primer is written primarily for the nonstatistician, although formal mathematical details are provided in Appendix 1.
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3

Stoica, Petre, and Torsten Söderström. "Partial Least Squares: A First‐order Analysis." Scandinavian Journal of Statistics 25, no. 1 (March 1998): 17–24. http://dx.doi.org/10.1111/1467-9469.00085.

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4

Kumar, S., U. Kruger, E. B. Martin, and A. J. Morris. "Analysis of Nonlinear Partial Least Squares Algorithms." IFAC Proceedings Volumes 37, no. 9 (July 2004): 739–44. http://dx.doi.org/10.1016/s1474-6670(17)31898-0.

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5

Qing Wang, Feng Chen, Wenli Xu, and Ming-Hsuan Yang. "Object Tracking via Partial Least Squares Analysis." IEEE Transactions on Image Processing 21, no. 10 (October 2012): 4454–65. http://dx.doi.org/10.1109/tip.2012.2205700.

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6

Jin, Xi, Xing Zhang, Kaifeng Rao, Liang Tang, and Qiwei Xie. "Semi-supervised partial least squares." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 03 (January 13, 2020): 2050014. http://dx.doi.org/10.1142/s0219691320500149.

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Traditional supervised dimensionality reduction methods can establish a better model often under the premise of a large number of samples. However, in real-world applications where labeled data are scarce, traditional methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method by using partial least squares (PLS) which we call semi-supervised partial least squares (S2PLS). To combine the labeled and unlabeled samples into a S2PLS model, we first apply the PLS algorithm to unsupervised dimensionality reduction. Then, the final S2PLS model is established by ensembling the supervised PLS model and the unsupervised PLS model which using the basic idea of principal model analysis (PMA) method. Compared with unsupervised or supervised dimensionality reduction algorithms, S2PLS not only can improve the prediction accuracy of the samples but also enhance the generalization ability of the model. Meanwhile, it can obtain better results even there are only a few or no labeled samples. Experimental results on five UCI data sets also confirmed the above properties of S2PLS algorithm.
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7

Simonin, Dimitri, and Bernard Morard. "Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses." International Journal of Trade, Economics and Finance 8, no. 1 (February 2017): 1–11. http://dx.doi.org/10.18178/ijtef.2017.8.1.531.

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8

Arioli, Mario, Marc Baboulin, and Serge Gratton. "A Partial Condition Number for Linear Least Squares Problems." SIAM Journal on Matrix Analysis and Applications 29, no. 2 (January 2007): 413–33. http://dx.doi.org/10.1137/050643088.

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9

Alsberg, Bjørn K., Douglas B. Kell, and Royston Goodacre. "Variable Selection in Discriminant Partial Least-Squares Analysis." Analytical Chemistry 70, no. 19 (October 1998): 4126–33. http://dx.doi.org/10.1021/ac980506o.

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10

Nitzl, Christian, Jose L. Roldan, and Gabriel Cepeda. "Mediation analysis in partial least squares path modeling." Industrial Management & Data Systems 116, no. 9 (October 17, 2016): 1849–64. http://dx.doi.org/10.1108/imds-07-2015-0302.

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Purpose Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues. Design/methodology/approach This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM). Findings This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects. Originality/value The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.
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11

Krystal, Andrew D., Henry S. Greenside, Paul S. Rapp, Alfonso Albano, Chris Cellucci, and Richard D. Weiner. "PARTIAL LEAST SQUARES ANALYSIS OF MULTICHANNEL EEG DATA." Journal of Clinical Neurophysiology 15, no. 3 (May 1998): 274. http://dx.doi.org/10.1097/00004691-199805000-00028.

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12

Reeves, James B., and Stephen R. Delwiche. "SAS® Partial Least Squares for Discriminant Analysis." Journal of Near Infrared Spectroscopy 16, no. 1 (January 2008): 31–38. http://dx.doi.org/10.1255/jnirs.757.

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13

Gallo, Michele. "Discriminant partial least squares analysis on compositional data." Statistical Modelling: An International Journal 10, no. 1 (April 2010): 41–56. http://dx.doi.org/10.1177/1471082x0801000103.

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14

Haenlein, Michael, and Andreas M. Kaplan. "A Beginner's Guide to Partial Least Squares Analysis." Understanding Statistics 3, no. 4 (November 2004): 283–97. http://dx.doi.org/10.1207/s15328031us0304_4.

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15

Dunn, W. J., D. R. Scott, and W. G. Glen. "Principal components analysis and partial least squares regression." Tetrahedron Computer Methodology 2, no. 6 (1989): 349–76. http://dx.doi.org/10.1016/0898-5529(89)90004-3.

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16

Poerio, Dominic V., and Steven D. Brown. "Stacked interval sparse partial least squares regression analysis." Chemometrics and Intelligent Laboratory Systems 166 (July 2017): 49–60. http://dx.doi.org/10.1016/j.chemolab.2017.03.006.

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17

Kettaneh-Wold, Nouna. "Analysis of mixture data with partial least squares." Chemometrics and Intelligent Laboratory Systems 14, no. 1-3 (April 1992): 57–69. http://dx.doi.org/10.1016/0169-7439(92)80092-i.

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18

Johnson, Kjell, and William Rayens. "Influence function analysis applied to partial least squares." Computational Statistics 22, no. 2 (February 24, 2007): 293–306. http://dx.doi.org/10.1007/s00180-007-0037-0.

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19

Alın, Aylin, Serdar Kurt, Anthony Randal McIntosh, Adile Oniz, and Murat Ozg¨oren ¨. "Partial Least Squares Analysis in Electrical Brain Activity." Journal of Data Science 7, no. 1 (July 10, 2021): 99–110. http://dx.doi.org/10.6339/jds.2009.07(1).434.

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20

Haaland, David M., and David K. Melgaard. "New Classical Least-Squares/Partial Least-Squares Hybrid Algorithm for Spectral Analyses." Applied Spectroscopy 55, no. 1 (January 2001): 1–8. http://dx.doi.org/10.1366/0003702011951353.

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21

Wei Zhang, Wei Zhang, Lianfei Duan Lianfei Duan, Luozheng Zhang Luozheng Zhang, Yujun Zhang Yujun Zhang, Liuyi Ling Liuyi Ling, and Yunjun Yang Yunjun Yang. "X-ray fluorescence spectra quantitative analysis based on characteristic spectra optimization of partial least-squares method." Chinese Optics Letters 12, s2 (2014): S23001–323005. http://dx.doi.org/10.3788/col201412.s23001.

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22

Wu, Ju. "Research on Several Problems in Partial Least Squares Regression Analysis." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 754–58. http://dx.doi.org/10.2174/1874129001408010754.

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Purpose: preliminary discussion on model prediction precision in the partial least squares regression analysis method; Method: introduce current development conditions of partial least squares regression analysis, analyze problems of traditional regression analysis method such as multiple linear regression analysis, introduce the mathematic principle and modeling method of the partial least squares regression analysis method, and conduct detailed analysis on the partial least squares regression analysis modeling and prediction by using the classical Linnerud data. Result: The partial least squares regression analysis has the basic features of the multiple linear regression analysis and principal component analysis, can precisely predict multiple data and establish a precise mathematical model; Conclusion: The partial least squares regression analysis can provide precise mathematical model and can reserve the explaining variants remarkably associated to explained variants to most extent, so it is feasible to some extent and can meet the general requirements of engineering, economy, biology and medical statistical analysis.
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23

Kurniawan, Arif, Loekito Loekito, and Solimun Solimun. "Power Of Test Path Analysis and Partial Least Square Analysis." CAUCHY 4, no. 3 (November 30, 2016): 112. http://dx.doi.org/10.18860/ca.v4i3.3593.

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Path analysis Analysis and Partial Least Square (PLS) was used to analyze many variables. Both methods use the least squares method (OLS) that can be compared between the two to determine the best method in a study to get an assessment of the behavior of civil servants in the Government of Kediri.<br /> The purpose of this study is: comparing path analysis Analysis with Partial Least Square (PLS) on the power of the test and the valueR<sup>2</sup>.Path method is able to provide the value of R2 higher than Analysis of Partial Least Square (PLS) but the value of the test power analysisi path is smaller than using Analysis of Partial Least Square (PLS). Usage analysis methods Path Analysis and Partial Least Square (PLS) produces behavioral assessment of civil servants in the government of Kediri is nearly equal results and discussion. Based on the analysis to prove that the behavior of civil servants in the Government of Kediri not meet eligibility based on the grade levels and echelons of the civil service
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24

Jiang, F., Y. Yang, J. Li, W. Li, Y. Luo, Y. Li, H. Zhao, X. Wang, G. Yin, and G. Wu. "Partial least squares-based gene expression analysis in preeclampsia." Genetics and Molecular Research 14, no. 2 (2015): 6598–604. http://dx.doi.org/10.4238/2015.june.18.2.

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25

Zeng, Xue-Qiang, and Guo-Zheng Li. "Incremental partial least squares analysis of big streaming data." Pattern Recognition 47, no. 11 (November 2014): 3726–35. http://dx.doi.org/10.1016/j.patcog.2014.05.022.

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26

Martı́nez-Montes, Eduardo, Pedro A. Valdés-Sosa, Fumikazu Miwakeichi, Robin I. Goldman, and Mark S. Cohen. "Concurrent EEG/fMRI analysis by multiway Partial Least Squares." NeuroImage 22, no. 3 (July 2004): 1023–34. http://dx.doi.org/10.1016/j.neuroimage.2004.03.038.

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27

Gottfries, Johan, Kaj Blennow, Anders Wallin, and C. G. Gottfries. "Diagnosis of Dementias Using Partial Least Squares Discriminant Analysis." Dementia and Geriatric Cognitive Disorders 6, no. 2 (1995): 83–88. http://dx.doi.org/10.1159/000106926.

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28

Brereton, Richard G., and Gavin R. Lloyd. "Partial least squares discriminant analysis: taking the magic away." Journal of Chemometrics 28, no. 4 (March 18, 2014): 213–25. http://dx.doi.org/10.1002/cem.2609.

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29

Shinzawa, Hideyuki, Pitiporn Ritthiruangdej, and Yukihiro Ozaki. "Kernel Analysis of Partial Least Squares (PLS) Regression Models." Applied Spectroscopy 65, no. 5 (May 1, 2011): 549–56. http://dx.doi.org/10.1366/10-06187.

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30

Tutmez, Bulent. "Use of partial least squares analysis in concrete technology." Computers and Concrete 13, no. 2 (February 25, 2014): 173–85. http://dx.doi.org/10.12989/cac.2014.13.2.173.

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31

Wang, Yanxia, Hui Cao, Yan Zhou, and Yanbin Zhang. "Nonlinear partial least squares regressions for spectral quantitative analysis." Chemometrics and Intelligent Laboratory Systems 148 (November 2015): 32–50. http://dx.doi.org/10.1016/j.chemolab.2015.08.024.

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32

Yaroshchyk, P., D. L. Death, and S. J. Spencer. "Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS." J. Anal. At. Spectrom. 27, no. 1 (2012): 92–98. http://dx.doi.org/10.1039/c1ja10164a.

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33

Li, Zhong-ping. "An analysis of subhealth status based on partial least squares." Journal of Chinese Integrative Medicine 9, no. 2 (February 15, 2011): 148–52. http://dx.doi.org/10.3736/jcim20110206.

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34

Zheleznova, T. Iu, I. V. Vlasova, and A. V. Shilova. "Analysis of catalysts by spectrophotometry with partial least-squares method." Аналитика и контроль 19, no. 4 (2015): 363–72. http://dx.doi.org/10.15826/analitika.2015.19.4.004.

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35

HASEGAWA, KIYOSHI, HIROMICHI SHIGYOU, HIROYUKI SONOKI, YOSHIKATSU MIYASHITA, and SHIN-ICHI SASAKI. "QSAR ANALYSIS OF ANTIARRHYTHMIC PHENYLPYRIDINES USING PARTIAL LEAST SQUARES METHOD." Analytical Sciences 7, Supple (1991): 723–24. http://dx.doi.org/10.2116/analsci.7.supple_723.

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36

Rayens, William S., and Anders H. Andersen. "Multivariate analysis of fMRI data by oriented partial least squares." Magnetic Resonance Imaging 24, no. 7 (September 2006): 953–58. http://dx.doi.org/10.1016/j.mri.2006.03.007.

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37

McIntosh, Anthony Randal, and Nancy J. Lobaugh. "Partial least squares analysis of neuroimaging data: applications and advances." NeuroImage 23 (January 2004): S250—S263. http://dx.doi.org/10.1016/j.neuroimage.2004.07.020.

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38

Meng, Zhen, Shichang Zhang, Yan Yang, and Ming Liu. "Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/143965.

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Considering the different types of error and the nonlinearity of the meteorological measurement, this paper proposes a nonlinear partial least squares method for consistency analysis of meteorological data. For a meteorological element from one automated weather station, the proposed method builds the prediction model based on the corresponding meteorological elements of other surrounding automated weather stations to determine the abnormality of the measured values. For the proposed method, the latent variables of the independent variables and the dependent variables are extracted by the partial least squares (PLS), and then they are, respectively, used as the inputs and outputs of neural network to build the nonlinear internal model of PLS. The proposed method can deal with the limitation of traditional nonlinear PLS whose inner model is the fixed quadratic function or the spline function. Two typical neural networks are used in the proposed method, and they are the back propagation neural network and the adaptive neuro-fuzzy inference system (ANFIS). Moreover, the experiments are performed on the real data from the atmospheric observation equipment operation monitoring system of Shaanxi Province of China. The experimental results verify that the nonlinear PLS with the internal model of ANFIS has higher effectiveness and could realize the consistency analysis of meteorological data correctly.
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39

Polat, Esra, and Suleyman Gunay. "A New Approach to Robust Partial Least Squares Regression Analysis." International Journal of Mathematics Trends and Technology 9, no. 3 (May 25, 2014): 197–205. http://dx.doi.org/10.14445/22315373/ijmtt-v9p524.

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40

Johnson, Kjell, and William Rayens. "Influence function analysis for partial least squares with uncorrelated components." Statistics 40, no. 1 (February 2006): 65–93. http://dx.doi.org/10.1080/02331880500356564.

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41

Ding, Shuang, Yinhai Xu, Tingting Hao, and Ping Ma. "Partial least squares based gene expression analysis in renal failure." Diagnostic Pathology 9, no. 1 (2014): 137. http://dx.doi.org/10.1186/1746-1596-9-137.

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42

Manne, Rolf. "Analysis of two partial-least-squares algorithms for multivariate calibration." Chemometrics and Intelligent Laboratory Systems 2, no. 1-3 (August 1987): 187–97. http://dx.doi.org/10.1016/0169-7439(87)80096-5.

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43

Kumar, Keshav. "Partial Least Square (PLS) Analysis." Resonance 26, no. 3 (March 2021): 429–42. http://dx.doi.org/10.1007/s12045-021-1140-1.

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44

el Bouhaddani, Said, Hae-Won Uh, Caroline Hayward, Geurt Jongbloed, and Jeanine Houwing-Duistermaat. "Probabilistic partial least squares model: Identifiability, estimation and application." Journal of Multivariate Analysis 167 (September 2018): 331–46. http://dx.doi.org/10.1016/j.jmva.2018.05.009.

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45

Pen, Ue-Li. "Discrete least-squares global approximations to solutions of partial differential equations." Numerical Methods for Partial Differential Equations 7, no. 3 (1991): 303–15. http://dx.doi.org/10.1002/num.1690070308.

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46

Wu, Bang, Yunpeng Hu, Chuanhui Zhou, Guaiguai Chen, and Guannan Li. "Fault identification for chiller sensor based on partial least square method." E3S Web of Conferences 233 (2021): 03057. http://dx.doi.org/10.1051/e3sconf/202123303057.

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Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.
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47

Jiao, Long, Shan Bing, Xiaofeng Zhang, and Hua Li. "Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy." Journal of the Serbian Chemical Society 81, no. 2 (2016): 209–18. http://dx.doi.org/10.2298/jsc150227065j.

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The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.
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48

F. Hair Jr, Joe, Marko Sarstedt, Lucas Hopkins, and Volker G. Kuppelwieser. "Partial least squares structural equation modeling (PLS-SEM)." European Business Review 26, no. 2 (March 4, 2014): 106–21. http://dx.doi.org/10.1108/ebr-10-2013-0128.

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Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value – This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.
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49

Codd, A. L., T. A. Manteuffel, and S. F. McCormick. "Multilevel First-Order System Least Squares for Nonlinear Elliptic Partial Differential Equations." SIAM Journal on Numerical Analysis 41, no. 6 (January 2003): 2197–209. http://dx.doi.org/10.1137/s0036142902404406.

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50

Sarrafi, Amir H. M., Elahe Konoz, and Maryam Ghiyasvand. "Simultaneous Detemination of Atorvastatin Calcium and Amlodipine Besylate by Spectrophotometry and Multivariate Calibration Methods in Pharmaceutical Formulations." E-Journal of Chemistry 8, no. 4 (2011): 1670–79. http://dx.doi.org/10.1155/2011/292346.

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Resolution of binary mixture of atorvastatin (ATV) and amlodipine (AML) with minimum sample pretreatment and without analyte separation has been successfully achieved using a rapid method based on partial least square analysis of UV–spectral data. Multivariate calibration modeling procedures, traditional partial least squares (PLS-2), interval partial least squares (iPLS) and synergy partial least squares (siPLS), were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. The simultaneous determination of both analytes was possible by PLS processing of sample absorbance between 220-425 nm. The correlation coefficients (R) and root mean squared error of cross validation (RMSECV) for ATV and AML in synthetic mixture were 0.9991, 0.9958 and 0.4538, 0.2411 in best siPLS models respectively. The optimized method has been used for determination of ATV and AML in amostatin commercial tablets. The proposed method are simple, fast, inexpensive and do not need any separation or preparation methods.
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