Improved modelling for low-correlated multiple responses by common-subset-of-independent-variables partial-least-squares

The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s is 95,85% is greater than PCR’s = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS

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Application of Partial Least-Squares Regression in the Prediction of Concrete Strength Taking into Account Interactional Items Among Independent Variables

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.101-102.220 ◽

2011 ◽

Vol 101-102 ◽

pp. 220-223

Author(s):

Jian Ping Jiang

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Latent Variables ◽

Partial Least Squares Regression ◽

Concrete Strength ◽

Forecast Model ◽

Least Squares Regression ◽

Independent Variables ◽

Cementing Material ◽

Water Ratio

Based on partial least-squares regression taking into account interactional items among independent variables, this paper had a prediction on concrete strength at the 28th day. Taking proportion of flyash in cementing material, usage amount of cementing material, ash-water ratio as independent variables , and concrete strength at the 28th day as dependent variable , the forecast model of concrete strength was obtained. It was found that press residual value decreased with the increase of number of latent variables, and number of latent variables were three according to Press residual value versus number of latent variables. The normal regression coefficient of ash-water ratio was the largest in three influence factors, this indicated that the influence of ash-water ratio was largest to concrete strength at the 28th day; The determination coefficient of forecast model obtained in this paper was 0.9353, the error of forecast model was. The following conclusion can be drawn that, the model is accurate and credible, and the partial least-squares regression taking into account interactional items among independent variables is a eximious non-linear method, and it is worthy to spread its application in the forecast analysis of concrete strength at the 28th day.

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Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models in Partial Least Squares Modeling for Multiple Responses

Analytical Chemistry ◽

10.1021/ac400339e ◽

2013 ◽

Vol 85 (11) ◽

pp. 5444-5453 ◽

Cited By ~ 15

Author(s):

Jan P. M. Andries ◽

Yvan Vander Heyden ◽

Lutgarde M. C. Buydens

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Multiple Responses ◽

Variable Reduction

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External Deformation Monitoring and Improved Partial Least Squares Data Analysis Methods of High Core Rock-Fill Dam (HCRFD)

Sensors ◽

10.3390/s20020444 ◽

2020 ◽

Vol 20 (2) ◽

pp. 444

Author(s):

Xiang Cheng ◽

Qingquan Li ◽

Wei Zhou ◽

Zhiwei Zhou

Keyword(s):

Data Analysis ◽

Least Squares ◽

Water Level ◽

Partial Least Squares ◽

Small Angle ◽

Traditional Method ◽

Water Storage ◽

Deformation Monitoring ◽

Independent Variables ◽

Rock Fill

External deformation monitoring of high core rock-fill dams (HCRFDs) is an important and difficult part of safety monitoring. The traditional method of external deformation monitoring and data analysis for HCRFDs is to use a total station for small angle observations and establish a regression model to analyze the results. However, the small angle method has low accuracy and a low automation degree, and there is multicollinearity between the independent variables, which affects the parameter estimation and leads to the failure of model establishment. The angle forward intersection method is adopted in this paper for observation, and an improved partial least squares method (IPLS) is proposed to eliminate the multicollinearity of the independent variables. Compared to the traditional method, the improved observation method exhibits high accuracy and a high automation degree. The new data analysis method can not only eliminate multicollinearity but also improve the interpretation ability of the model. The data from the initial stage of water storage shows that the displacement increases with the increase in the upstream water level and time, and the speed of water storage is proportional to the displacement. The water level and time are the main influencing factors. This conclusion provides a theoretical basis for reservoir management departments to control water levels and gate opening and closing. The method in this paper can be applied to arch dams, gravity dams, and other types of waterpower engineering systems.

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Improved Multi-Class Discrimination by Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis

Talanta ◽

10.1016/j.talanta.2021.122595 ◽

2021 ◽

pp. 122595

Author(s):

Jan P.M. Andries ◽

Yvan Vander Heyden

Keyword(s):

Discriminant Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Independent Variables ◽

Class Discrimination

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The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v7i4.3766 ◽

2016 ◽

Vol 7 (4) ◽

pp. 325

Author(s):

Margaretha Ohyver ◽

Herena Pudjihastuti

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Wavelet Transformation ◽

Haar Wavelet ◽

Occupancy Rate ◽

Hotel Room ◽

Independent Variables ◽

Mexican Hat ◽

Reliable Model ◽

Pls Method

Partial Least Squares (PLS) method was developed in 1960 by Herman Wold. The method particularly suits with construct a regression model when the number of independent variables is many and highly collinear. The PLS can be combined with other methods, one of which is a Continuous Wavelet Transformation (CWT). By considering that the presence of outliers can lead to a less reliable model, and this kind of transformation may be required at a stage of pre-processing, the data is free of noise or outliers. Based on the previous study, Kendari hotel room occupancy rate was affected by the outlier, and it had a low value of R2. Therefore, this research aimed to obtain a good model by combining the PLS method and CWT transformation using the Mexican Hats them other wavelet of CWT. The research concludes that merging the PLS and the Mexican Hat transformation has resulted in a better model compared to the model that combined the PLS and the Haar wavelet transformation as shown in the previous study. The research shows that by changing the mother of the wavelet, the value of R2 can be improved significantly. The result provides information on how to increase the value of R2. The other advantage is the information for hotel managements to notice the age of the hotel, the maximum rates, the facilities, and the number of rooms to increase the number of visitors.

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An fMRI study examining spatial navigation through a virtual environment: Partial least squares analysis

PsycEXTRA Dataset ◽

10.1037/e705572011-044 ◽

2005 ◽

Author(s):

Richard Mraz ◽

Nancy J. Lobaugh ◽

Genevieve Quintin ◽

Konstantine K. Kakzanis ◽

Simon J. Graham

Keyword(s):

Least Squares ◽

Virtual Environment ◽

Partial Least Squares ◽

Spatial Navigation ◽

Partial Least Squares Analysis ◽

Fmri Study

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Controlling in der Produktentwicklung

Controlling ◽

10.15358/0935-0381-2020-3-45 ◽

2020 ◽

Vol 32 (3) ◽

pp. 45-50

Author(s):

Marc Janka

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Finite Mixture

Gemeinhin gilt die Annahme, dass das Controlling für viele deutsche Unternehmen auch oder besonders in der Produktentwicklung von großer Bedeutung ist und vor allem unter Umfeldunsicherheit ein wesentlicher Erfolgsfaktor sein kann. Der vorliegende Beitrag zeigt unter Anwendung einer für die Controlling-Forschung neuartigen Methode zur Schätzung von Mischverteilungen mittels partieller Regressionen (englisch finite mixture partial least squares [FIMIX-PLS]), ob diese Annahme für alle Unternehmen gleichermaßen gilt.

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