Journal articles: 'Partial least square analysis'

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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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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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Pangastuti, Sinta Septi, Tri Murniati, Alhassan Sessay, and Heri Kuswanto. "Partial Least Square Analysis for University Student Satisfaction." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 653–60. http://dx.doi.org/10.14421/icse.v3.581.

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A university needs to identify and analyse their students’ satisfaction to be able to compete with others. There are five dimensions to identify students’ satisfaction, such as reliability, assurance, empathy, responsiveness, and tangibles. Related to randomness of the data, primary data collected from stratified sampling tend to violate multivariate normality test. Therefore, partial least square (PLS) might be one alternative solution since it ignores multivariate normal and multicollinearity assumptions. As a result, tangible, assurance and empathy affect student satisfaction and student satisfaction significantly affect student achievement. Therefore, we recommend to university to improve service quality especially on tangible, assurance and empathy aspect to improve student satisfaction and student achievement.

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Serrano-Cinca, Carlos, and Begoña Gutiérrez-Nieto. "Partial Least Square Discriminant Analysis for bankruptcy prediction." Decision Support Systems 54, no. 3 (February 2013): 1245–55. http://dx.doi.org/10.1016/j.dss.2012.11.015.

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Zulkifli, Raudhah, Nazim Aimran, Sayang Mohd Deni, and Fatin Najihah Badarisam. "A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling." International Journal of Data and Network Science 6, no. 2 (2022): 391–400. http://dx.doi.org/10.5267/j.ijdns.2021.12.015.

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Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.

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Hu, Shih-Yao B., Amy Lillquist, Mark A. Arnold, and John M. Wiencek. "Partial Least Square Analysis of Lysozyme Near-Infrared Spectra." Applied Biochemistry and Biotechnology 87, no. 3 (2000): 153–64. http://dx.doi.org/10.1385/abab:87:3:153.

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Seweh, Emmanuel Amomba, Zou Xiaobo, Feng Tao, Shi Jiachen, Haroon Elrasheid Tahir, and Muhammad Arslan. "Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy." Journal of Near Infrared Spectroscopy 27, no. 3 (February 20, 2019): 220–31. http://dx.doi.org/10.1177/0967033519830061.

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A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

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Lubis, Muharman, Arif Ridho Lubis, and Ahmad Almaarif. "Exploring the Pattern of Voters’ Characteristics: Partial Least Square Analysis." Journal of Physics: Conference Series 1566 (June 2020): 012109. http://dx.doi.org/10.1088/1742-6596/1566/1/012109.

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Rizkia, Ajeng Dwi, Dwi Ispriyanti, and Sugito Sugito. "PENGARUH KUALITAS LAYANAN DAN CITRA MEREK TERHADAP KEPUASAN PENGGUNA YOUTUBE PREMIUM MENGGUNAKAN PARTIAL LEAST SQUARE." Jurnal Gaussian 11, no. 3 (July 19, 2022): 323–31. http://dx.doi.org/10.14710/j.gauss.11.3.323-331.

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As one of the largest digital service providers in the world, YouTube certainly makes breakthroughs to maintain user interest in accessing videos through YouTube, one of which is by creating the YouTube Premium service. This research was conducted to determine the extent to which these services can provide a sense of satisfaction for its users, because as a digital service provider company, YouTube is very dependent on user satisfaction. User satisfaction is influenced by service quality and brand image. In this study, service quality, brand image, and service user satisfaction act as latent variables. To test the predictive relationship between indicator variables and variables that cannot be measured directly (latent variables) by seeing whether there is a relationship or influence between these variables using the obtained modeling can be done using the Partial Least Square method. Therefore, to determine the effect of service quality and brand image on YouTube Premium user satisfaction, an analysis was conducted using the Partial Least Square method. The research data was obtained by distributing questionnaires to 150 YouTube Premium users in Indonesia. The results of the analysis show that service quality and brand image have a significant effect on YouTube Premium user satisfaction.

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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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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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Wang, Zhuo, Jian Feng Xu, and Ran Hu. "Explore Medicine Using Compression and Partial Least Square Discriminate Analysis Method." Applied Mechanics and Materials 220-223 (November 2012): 2694–97. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2694.

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The aim to explore the different medicine have different or similar effect, the paper put forward explore medicine using compression and partial least square discriminate analysis method. First of these to data preprocessing using wavelet compression ,the second to classify and train the sample of medicine based on PLS-DA,the third to discriminate the medicine which unknown type. The result indicate that the different medicine have different or similar effect ,the new medicine may classify based on effect using PLS-DA. The method was proved to be feasible and effective after tested with 8 kinds of medicine experimental data.

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Ivantoro, Tommy, and Bambang Syairudin. "Analysis of Factors Affecting Operator Performance with Partial Least Square Approach." IPTEK Journal of Proceedings Series, no. 3 (October 14, 2021): 24. http://dx.doi.org/10.12962/j23546026.y2020i3.11072.

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Enegbuma, Wallace Imoudu, Andrew Chukwuyem Ologbo, Godwin Uche Aliagha, and Kherun Nita Ali. "Partial least square analysis of building information modelling impact in Malaysia." International Journal of Product Lifecycle Management 8, no. 4 (2015): 311. http://dx.doi.org/10.1504/ijplm.2015.075928.

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Tang, Jian, Lijie Zhao, Heng Yue, Wen Yu, and Tianyou Chai. "Vibration Analysis Based on Empirical Mode Decomposition and Partial Least Square." Procedia Engineering 16 (2011): 646–52. http://dx.doi.org/10.1016/j.proeng.2011.08.1136.

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Shivhare, Ravi, and Nitin Mishra. "Data Hiding on Selected Object Track using Partial Least Square Analysis." International Journal of Computer Applications 132, no. 1 (December 17, 2015): 37–41. http://dx.doi.org/10.5120/ijca2015907270.

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Ji, Guoli, Guangzao Huang, Zijiang Yang, Xiaohui Wu, Xiaojing Chen, and Mingshun Yuan. "Using consensus interval partial least square in near infrared spectra analysis." Chemometrics and Intelligent Laboratory Systems 144 (May 2015): 56–62. http://dx.doi.org/10.1016/j.chemolab.2015.03.008.

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Wang, Hai Tao, Zhen Wen Xu, Bin Wang, and Heng Li. "Application of Coupling Model of Projection Pursuit Partial Least-Square Regression Based on Real Coded Accelerating Genetic Algorithm in Land Use Change Forecasting." Advanced Materials Research 347-353 (October 2011): 1774–77. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.1774.

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In study of Land Use Change forecasting, lots of methods have been developed ,such as Markov model、BP algorithm、Canonical correlation analysis, least-squares regression analysis ,but these methods have deficiency in decision and often inadequate in sample size. In response to these deficiencies，projection pursuit Partial Least-Square Regression based on real coded accelerating genetic algorithm model is developed to analyze and predict land use change in Yanji City. The computation results show that the relative error of Coupling Model of Partial Least-Square Regression Based on Projection Pursuit is smaller than traditional Partial Least-Square Regression model’s, and it has improved the prediction precision evidently.

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Peng, Dan, and Qing Chen Nie. "A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy." Advanced Materials Research 765-767 (September 2013): 528–31. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.528.

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To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

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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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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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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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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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KUSUMA, PANDE PUTU BUDI, and I. GUSTI AYU MADE SRINADI. "PREDIKSI WAKTU KETAHANAN HIDUP DENGAN METODE PARTIAL LEAST SQUARE." E-Jurnal Matematika 2, no. 1 (January 30, 2013): 49. http://dx.doi.org/10.24843/mtk.2013.v02.i01.p028.

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Coronary heart disease is caused due to an accumulation of fat on the inside walls of blood vessels of the heart (coronary arteries). The factors that had led to the occurrence of coronary heart disease is dominated by unhealthy lifestyle of patients, and the survival times of different patients. This research objective is to predict the survival time of patients with coronary heart disease by taking into account the explanatory variables were analyzed by the method of Partial Least Square (PLS). PLS method is used to resolve the multiple regression analysis when the specific problems of multicollinearity and microarray data. The purpose of the PLS method is to predict the explanatory variables with multiple response variables so as to produce a more accurate predictive value. The results of this research showed that the prediction of survival for the three samples of patients with coronary heart disease had an average of 13 days, with a RMSEP value (error value) was 1.526 which means that the results of this study are not much different from the predicted results in the field of medicine. This is consistent with the fact that the medical field suggests that the average survival for patients with coronary heart disease by 13 days.

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Lorenzo, Hadrien, Olivier Cloarec, Rodolphe Thiébaut, and Jérôme Saracco. "Data‐driven sparse partial least squares." Statistical Analysis and Data Mining: The ASA Data Science Journal 15, no. 2 (October 18, 2021): 264–82. http://dx.doi.org/10.1002/sam.11558.

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Deepak, Dharmpal, CHANDAN DEEP SINGH, and Jasvinder Singh. "Analysis of green manufacturing attributes through partial least square structural equation modelling." International Journal of Internet Manufacturing and Services 9, no. 2 (2023): 1. http://dx.doi.org/10.1504/ijims.2023.10049132.

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Máquina, Ademar Domingos Viagem, Letícia Maria de Souza, Lucas Caixeta Gontijo, Douglas Queiroz Santos, and Waldomiro Borges Neto. "Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis." Analytical Letters 50, no. 13 (June 22, 2017): 2117–28. http://dx.doi.org/10.1080/00032719.2016.1267186.

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Le Bihan, Yann, József Pávó, and Claude Marchand. "Partial least square regression: an analysis tool for quantitative non-destructive testing." European Physical Journal Applied Physics 67, no. 3 (August 13, 2014): 30901. http://dx.doi.org/10.1051/epjap/2014130487.

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Huang, Shian-Chang, Nan-Yu Wang, and Tung-Kuang Wu. "High Dimensional Data Mining Systems by Kernel Orthonormalized Partial Least Square Analysis." International Journal of Future Computer and Communication 4, no. 5 (2015): 364–67. http://dx.doi.org/10.18178/ijfcc.2015.4.5.417.

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Kovacevic, Natasa, and Anthony Randal McIntosh. "Groupwise independent component decomposition of EEG data and partial least square analysis." NeuroImage 35, no. 3 (April 2007): 1103–12. http://dx.doi.org/10.1016/j.neuroimage.2007.01.016.

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Wang, Songhui, Bingren Xiang, Yilong Su, and Qianqian Tang. "Direct determination of dichlorvos in water by partial least square-discriminant analysis." Environmental Chemistry Letters 10, no. 4 (April 4, 2012): 383–87. http://dx.doi.org/10.1007/s10311-012-0363-5.

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Yang, Hai-lan, Yan Cai, Ye-feng Bao, and Yun Zhou. "Analysis and application of partial least square regression in arc welding process." Journal of Central South University of Technology 12, no. 4 (August 2005): 453–58. http://dx.doi.org/10.1007/s11771-005-0181-z.

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SUNDARI, NI WAYAN ARI, I. GUSTI AYU MADE SRINADI, and MADE SUSILAWATI. "PENERAPAN METODE PARTIAL LEAST SQUARE REGRESSION (PLSR) PADA KASUS SKIZOFRENIA." E-Jurnal Matematika 10, no. 2 (May 30, 2021): 137. http://dx.doi.org/10.24843/mtk.2021.v10.i02.p333.

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Partial Least Square Regression (PLSR) is a method that combines principal component analysis and multiple linear regression, which aims to predict or analyze the dependent variable and more than one independent variable. The purpose of this study is to determine the equation model for the recurrence of schizophrenia patients using the PLSR method. The best number of components to form a PLSR model in this study is one component with a minimum RMSEP value of 0.6094 and an adjR2 value of 80.09 percent.

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Lee, Ke Hwa, and Shih Chih Che. "Introduction to Partial Least Square: Common Criteria and Practical Considerations." Advanced Materials Research 779-780 (September 2013): 1766–69. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1766.

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In the social science research area, there are two important statistical methodologies, one is covariance-based structural equation modeling (CBSEM), and the other one is variance-based partial least square (PLS). Compared with CBSEM, PLS lacks comparatively the reference books and full applications. The main purpose of this study is to develop a paradigm to demonstrate how to assess the reliability, convergent validity, discriminant validity, and path analysis in a proposed research model by using Smart PLS. We hope this study’s result can offer some correct steps when using PLS.

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Lenggogeni, Sari. "Why Is The Partial Least Square Important For Tourism Studies." International Journal of Tourism, Heritage and Recreation Sport 1, no. 2 (December 30, 2019): 7–15. http://dx.doi.org/10.24036/ijthrs.v1i2.27.

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Although the multiple regression method has been applied to exploratory research on most tourism studies, there is lack of understanding on studies that present a well-justified rationale in choosing a robust statistical tool for data analysis. This research note aims to review why tourism researchers are encouraged to use the Partial Least Squares Structural Equation Modelling (PLS-SEM) method to address this research problem. This article provides rationale, comparisons among techniques for multiple regression-based papers and suggestions for tourism researchers to justify why PLS-SEM is important for exploratory studies.

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Zhang, Wei, Hang Song, Jing Lu, Wen Liu, Lirong Nie, and Shun Yao. "Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate." International Journal of Analytical Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/145315.

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Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R2) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.

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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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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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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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Berutu, Trisnawati Gusnawita, Abdul Hoyyi, and Sugito Sugito. "ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS)." Jurnal Gaussian 7, no. 4 (November 30, 2018): 361–72. http://dx.doi.org/10.14710/j.gauss.v7i4.28863.

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Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%.

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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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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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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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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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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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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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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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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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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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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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Journal articles on the topic 'Partial least square analysis'

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