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Journal articles on the topic 'Principal component regression and principal component analysis'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Tucker, J. Derek, John R. Lewis, and Anuj Srivastava. "Elastic functional principal component regression." Statistical Analysis and Data Mining: The ASA Data Science Journal 12, no. 2 (2018): 101–15. http://dx.doi.org/10.1002/sam.11399.

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2

Shin, Jae-Kyoung, Tomoyuki Tarumi, and Yutaka Tanaka. "SENSITIVITY ANALYSIS IN PRINCIPAL COMPONENT REGRESSION." Japanese Journal of Biometrics 10 (1989): 57–68. http://dx.doi.org/10.5691/jjb.10.57.

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3

Liu, R. X., J. Kuang, Q. Gong, and X. L. Hou. "Principal component regression analysis with spss." Computer Methods and Programs in Biomedicine 71, no. 2 (2003): 141–47. http://dx.doi.org/10.1016/s0169-2607(02)00058-5.

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4

Devi, S. Loidang, and Ksh Radheshyam Singh. "A principal component regression analysis in agriculture." Bulletin of Pure & Applied Sciences- Mathematics and Statistics 33e, no. 2 (2014): 105. http://dx.doi.org/10.5958/2320-3226.2014.00003.4.

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5

Kyung, Minjung. "Bayesian analysis of principal component regression model." Journal of the Korean Data And Information Science Society 30, no. 2 (2019): 247–59. http://dx.doi.org/10.7465/jkdi.2019.30.2.247.

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6

Kyi, Lai Lai Khine, and Thi Soe Nyunt Thi. "Predictive geospatial analytics using principal component regression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2651–58. https://doi.org/10.11591/ijece.v10i3.pp2651-2658.

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Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression

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7

Hasegawa, Takeshi. "Spectral Simulation Study on the Influence of the Principal Component Analysis Step on Principal Component Regression." Applied Spectroscopy 60, no. 1 (2006): 95–98. http://dx.doi.org/10.1366/000370206775382749.

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8

Kyung, Minjung. "Bayesian analysis of quantile principal component regression model." Journal of the Korean Data And Information Science Society 32, no. 4 (2021): 739–55. http://dx.doi.org/10.7465/jkdi.2021.32.4.739.

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9

KONDO, Tadashi. "Revised GMDH Algorithm Using Principal Component-Regression Analysis." Transactions of the Institute of Systems, Control and Information Engineers 5, no. 10 (1992): 391–99. http://dx.doi.org/10.5687/iscie.5.391.

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10

Dangar, Nikhil, and Pravin Vataliya. "Principal component regression analysis to predict lifetime milk yield of Jaffarabadi buffaloes." Buffalo Bulletin 43, no. 3 (2024): 441–49. http://dx.doi.org/10.56825/bufbu.2024.4334036.

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The study aims to devise most appropriate prediction model for lifetime milk production of Jaffarabadi Buffalo, based on principal components formulated on initially expressed lactation records as predictors. Lactation milk yield, lactation period and peak milk yield records of first, second and third lactations of animals under study were used of 24 years (1987 to 2010). Principal components (PCs) were derived from data set using principal component regression analysis (PCRA), the principal components were used as predictors for predicting lifetime milk yield (LTMY). Multiple linear regressio

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11

Hunter, Michael A., and Yoshio Takane. "Constrained Principal Component Analysis: Various Applications." Journal of Educational and Behavioral Statistics 27, no. 2 (2002): 105–45. http://dx.doi.org/10.3102/10769986027002105.

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Constrained Principal Component Analysis (CPCA) is a method for structural analysis of multivariate data. It combines regression analysis and principal component analysis into a unified framework. This article provides example applications of CPCA that illustrate the method in a variety of contexts common to psychological research. We begin with a straightforward situation in which the structure of a set of criterion variables is explored using a set of predictor variables as row (subjects) constraints. We then illustrate the use of CPCA using constraints on the columns of a set of dependent v

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12

Weakley, Andrew Todd, P. C. Temple Warwick, Thomas E. Bitterwolf, and D. Eric Aston. "Multivariate Analysis of Micro-Raman Spectra of Thermoplastic Polyurethane Blends Using Principal Component Analysis and Principal Component Regression." Applied Spectroscopy 66, no. 11 (2012): 1269–78. http://dx.doi.org/10.1366/12-06588.

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13

Kawano, Shuichi. "Sparse principal component regression via singular value decomposition approach." Advances in Data Analysis and Classification 15, no. 3 (2021): 795–823. http://dx.doi.org/10.1007/s11634-020-00435-2.

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AbstractPrincipal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss

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14

Khine, Kyilai Lai, and ThiThi Soe Nyunt. "Predictive geospatial analytics using principal component regression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2651. http://dx.doi.org/10.11591/ijece.v10i3.pp2651-2658.

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Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression

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15

Liu, Zhining, Chengyun Song, Hanpeng Cai, Xingmiao Yao, and Guangmin Hu. "Enhanced coherence using principal component analysis." Interpretation 5, no. 3 (2017): T351—T359. http://dx.doi.org/10.1190/int-2016-0194.1.

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Coherence is a measure of similarity between seismic waveforms. It gives a quantitative description of lateral reflection changes and highlights variations of the geologic features within a seismic image. However, subtle changes in waveforms are often difficult to capture using traditional coherence measures because of the high similarity among the remaining parts in the vertical analysis window. We have developed an attribute called enhanced coherence based on principal component analysis (PCA) with the goal of reducing redundancy within the vertical analysis window, which is often composed o

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16

Næs, Tormod, and Harald Martens. "Principal component regression in NIR analysis: Viewpoints, background details and selection of components." Journal of Chemometrics 2, no. 2 (1988): 155–67. http://dx.doi.org/10.1002/cem.1180020207.

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17

Adami, G., G. Orsolini, A. Fassio, et al. "POS0474 FACTORS ASSOCIATED WITH EROSIVE RHEUMATOID ARTHRITIS, A MULTIMARKER PRINCIPAL COMPONENT ANALYSIS (PCA) AND PRINCIPAL COMPONENT REGRESSION (PCR) ANALYSIS." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 497–98. http://dx.doi.org/10.1136/annrheumdis-2023-eular.3624.

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BackgroundVarious clinical (disease activity, seropositive RA etc.) and metabolic risk factors (Dkk1 etc.) have been associated with erosive rheumatoid arthritis (RA). However, such risk factors might be intertwined, and multicollinearity might reduce our ability to discern the individual contribution to erosive score. Principal component analysis (PCA) is statistical technique for reducing dataset’s dimension and principal component regression (PCR) is a regression analysis based on PCA. PCR overcomes the multicollinearity problem.ObjectivesTo investigate the clinical and bone metabolic risk

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18

Shankar, P. Sai, J. V. Narasimham, and G. Ananthan. "Application of principal component regression analysis in agricultural studies." INTERNATIONAL RESEARCH JOURNAL OF AGRICULTURAL ECONOMICS AND STATISTICS 10, no. 1 (2019): 59–64. http://dx.doi.org/10.15740/has/irjaes/10.1/59-64.

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19

Sun, Jianguo. "A correlation principal component regression analysis of NIR data." Journal of Chemometrics 9, no. 1 (1995): 21–29. http://dx.doi.org/10.1002/cem.1180090104.

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20

Sun, Jianguo. "A multivariate principal component regression analysis of NIR data." Journal of Chemometrics 10, no. 1 (1996): 1–9. http://dx.doi.org/10.1002/(sici)1099-128x(199601)10:1<1::aid-cem397>3.0.co;2-0.

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21

Vaira, Stella, Víctor E. Mantovani, Juan C. Robles, Juan C. Sanchis, and Héctor C. Goicoechea. "Use of Chemometrics: Principal Component Analysis (PCA) and Principal Component Regression (PCR) for the Authentication of Orange Juice." Analytical Letters 32, no. 15 (1999): 3131–41. http://dx.doi.org/10.1080/00032719908543031.

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22

Amokaha, O.A, B. B. Apeagee, I. O. Agada, and A. M. Akosu. "A Principal Component Regression Approach to Determine the Relationship Between Yam Yield and Some Climatic Determinants." International Journal of Scientific Development and Research 8, no. 5 (2023): 754–65. https://doi.org/10.5281/zenodo.8373693.

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This work applied the principal component regression approach in modelling the relationship between yam yield and some climatic variables in Makurdi, Benue State, Nigeria. Secondary data were sourced from Benue Agricultural and Rural Development Authority (BNARDA) and Nigerian Meteorological Agency Headquarters, Tactical Air Command, Makurdi – Airport, Benue State. It was established that there was multi-collinearity among the climatic variables. In this study, an attempt has been made to apply the concept of Principal Component Regression as a remedial solution to this problem. After es

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23

Siburian, Jeffri Nelwin J. O., Rita Rahmawati, and Abdul Hoyyi. "REGRESI KOMPONEN UTAMA ROBUST S-ESTIMATOR UNTUK ANALISIS PENGARUH JUMLAH PENGANGGURAN DI JAWA TENGAH." Jurnal Gaussian 8, no. 4 (2019): 439–50. http://dx.doi.org/10.14710/j.gauss.v8i4.26724.

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Robust principal component regression s-estimator is principal component regression that applies robust approach method at principal component analysis and s-estimator at principal component regression analysis. The aim of robust principal component regression s-estimator is to overcome multicollinearity problems in multiple linier regression Ordinary Least Square (OLS) and to overcome outlier problems in principal component regression so get the most effective model. Minimum Volume Ellipsoid (MVE) is one of the robust approach methods that applied when doing principal component analysis and S

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24

Chen, Zhihao, and Jingmin Ji. "Analysis and Identification of the Composition of Ancient Glass Objects Based on Logistic Regression Analysis." Highlights in Science, Engineering and Technology 41 (March 30, 2023): 265–70. http://dx.doi.org/10.54097/hset.v41i.6830.

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Ancient glass objects were weathered by environmental influences, and the analysis of their main components and identification of their categories is a prerequisite for all subsequent research work. This paper uses Spearman's correlation coefficient and chi-square test of variance to analyses the correlation between weathering and type, color and decoration on the surface of cultural relics; secondly, descriptive statistical tests are conducted separately for two different types of glass objects to obtain the relationship between the changes in the content of each chemical component of differe

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25

Shang, Han Lin. "DYNAMIC PRINCIPAL COMPONENT REGRESSION: APPLICATION TO AGE-SPECIFIC MORTALITY FORECASTING." ASTIN Bulletin 49, no. 03 (2019): 619–45. http://dx.doi.org/10.1017/asb.2019.20.

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AbstractIn areas of application, including actuarial science and demography, it is increasingly common to consider a time series of curves; an example of this is age-specific mortality rates observed over a period of years. Given that age can be treated as a discrete or continuous variable, a dimension reduction technique, such as principal component analysis (PCA), is often implemented. However, in the presence of moderate-to-strong temporal dependence, static PCA commonly used for analyzing independent and identically distributed data may not be adequate. As an alternative, we consider a dyn

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26

Mbaluka, Morris Kateeti, Dennis K. Muriithi, and Gladys G. Njoroge. "Application of Principal Component Analysis and Hierarchical Regression Model on Kenya Macroeconomic Indicators." European Journal of Mathematics and Statistics 3, no. 1 (2022): 24–35. http://dx.doi.org/10.24018/ejmath.2022.3.1.74.

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The aim of this paper was to apply Principal Component Analysis (PCA) and hierarchical regression model on Kenyan Macroeconomic variables. The study adopted a mixed research design (descriptive and correlational research designs). The 18 macroeconomic variables data were extracted from Kenya National Bureau of Statistics and World Bank for the period 1970 to 2019. The R software was utilized to conduct all the data analysis. Principal Component Analysis was used to reduce the dimensionality of the data, where the original data set matrix was reduced to Eigenvectors and Eigenvalues. A hierarchi

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27

Zhou, Zheng Zhu, Xiao Yi Jin, Xiang Wei Zhang, and Yu Yi Lin. "Friction Properties Study of Metals by Principal Component Analysis." Applied Mechanics and Materials 711 (December 2014): 231–34. http://dx.doi.org/10.4028/www.scientific.net/amm.711.231.

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Based on MMW-1A vertical multifunctional friction and wear tester for the study,taking steel 45 as the research object, randomly changing the experiment load, speed, sliding distance and the size of the contact area, then the data we collect are processed and analyzed by principal component analysis, and obtained linear regression models by principal component regression, regression model has been tested with good fitting effect. The results showed that the principal component analysis method is also suitable for experimental study of friction and wear, explore new methods in the analysis of t

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28

Han, Xuanli, Jigen Peng, Angang Cui, and Fujun Zhao. "Sparse Principal Component Analysis via Fractional Function Regularity." Mathematical Problems in Engineering 2020 (August 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/7874140.

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In this paper, we describe a novel approach to sparse principal component analysis (SPCA) via a nonconvex sparsity-inducing fraction penalty function SPCA (FP-SPCA). Firstly, SPCA is reformulated as a fraction penalty regression problem model. Secondly, an algorithm corresponding to the model is proposed and the convergence of the algorithm is guaranteed. Finally, numerical experiments were carried out on a synthetic data set, and the experimental results show that the FP-SPCA method is more adaptable and has a better performance in the tradeoff between sparsity and explainable variance than S

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29

Faizia, Tsania, Alan Prahutama, and Hasbi Yasin. "PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST." Jurnal Gaussian 8, no. 2 (2019): 253–71. http://dx.doi.org/10.14710/j.gauss.v8i2.26670.

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Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Deve

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30

Goldsmith, Jeff, Vadim Zipunnikov, and Jennifer Schrack. "Generalized multilevel function-on-scalar regression and principal component analysis." Biometrics 71, no. 2 (2015): 344–53. http://dx.doi.org/10.1111/biom.12278.

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31

KONDO, Tadashi. "Multiinput-Multioutput Type GMDH Algorithm Using Regression-Principal Component Analysis." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 11 (1993): 520–29. http://dx.doi.org/10.5687/iscie.6.520.

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32

Keithley, Richard B., R. Mark Wightman, and Michael L. Heien. "Multivariate concentration determination using principal component regression with residual analysis." TrAC Trends in Analytical Chemistry 28, no. 9 (2009): 1127–36. http://dx.doi.org/10.1016/j.trac.2009.07.002.

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33

Nie, Yunlong, and Jiguo Cao. "Sparse functional principal component analysis in a new regression framework." Computational Statistics & Data Analysis 152 (December 2020): 107016. http://dx.doi.org/10.1016/j.csda.2020.107016.

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34

Hattab, Mohammad W., Charles S. Jackson, and Gabriel Huerta. "Analysis of climate sensitivity via high-dimensional principal component regression." Communications in Statistics: Case Studies, Data Analysis and Applications 5, no. 4 (2019): 394–414. http://dx.doi.org/10.1080/23737484.2019.1670119.

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35

Bae, Jae-Young, Jin-Mok Lee, and Jea-Young Lee. "Predicting Korea Pro-Baseball Rankings by Principal Component Regression Analysis." Communications for Statistical Applications and Methods 19, no. 3 (2012): 367–79. http://dx.doi.org/10.5351/ckss.2012.19.3.367.

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36

Okonkwo, EN, JU Okeke, and JC Nwabueze. "Principal Component Regression Analysis of CO₂ Emission." Bayero Journal of Pure and Applied Sciences 6, no. 1 (2014): 27. http://dx.doi.org/10.4314/bajopas.v6i1.6.

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37

Blanco, M., J. Coello, F. González, H. Iturriaga, and S. Maspoch. "Spectrophotometric Analysis of a Pharmaceutical Preparation by Principal Component Regression." Journal of Pharmaceutical Sciences 82, no. 8 (1993): 834–37. http://dx.doi.org/10.1002/jps.2600820816.

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38

Malik, Mohammad Rafi, Benjamin J. Isaac, Axel Coussement, Philip J. Smith, and Alessandro Parente. "Principal component analysis coupled with nonlinear regression for chemistry reduction." Combustion and Flame 187 (January 2018): 30–41. http://dx.doi.org/10.1016/j.combustflame.2017.08.012.

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39

Rosipal, Roman, and Mark Girolami. "An Expectation-Maximization Approach to Nonlinear Component Analysis." Neural Computation 13, no. 3 (2001): 505–10. http://dx.doi.org/10.1162/089976601300014439.

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The proposal of considering nonlinear principal component analysis as a kernel eigenvalue problem has provided an extremely powerful method of extracting nonlinear features for a number of classification and regression applications. Whereas the utilization of Mercer kernels makes the problem of computing principal components in, possibly, infinite-dimensional feature spaces tractable, there are still the attendant numerical problems of diagonalizing large matrices. In this contribution, we propose an expectation-maximization approach for performing kernel principal component analysis and show

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40

Liang, Qiwei, and Alan J. Thomson. "Habitat–abundance relationships of the earthworm Eisenia rosea (Savigny) (Lumbricidae), using principal component regression analysis." Canadian Journal of Zoology 72, no. 7 (1994): 1354–61. http://dx.doi.org/10.1139/z94-178.

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Principal component regression analysis was used to investigate the relationships between the abundance of the earthworm Eisenia rosea and soil characteristics at two Ontario locations. To this end we summarized our environmental data matrix with principal component analysis and then used the first several principal components in a multiple regression analysis. This two-step procedure remedies problems associated with multicollinearity among our environmental variables. At one location, moisture was the main factor correlating with the abundance of E. rosea. At the other location, because high

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Wu, Peilin, Qunying Zhang, Luzhao Chen, Wanhua Zhu, and Guangyou Fang. "Aeromagnetic Compensation Algorithm Based on Principal Component Analysis." Journal of Sensors 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/5798287.

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Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix i

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42

Nargundkar, Aniket, Vikas Gulia, and Anirban Sur. "Multi-Variate Analysis of Shell & Tube Heat Exchanger using Principal Component Analysis." E3S Web of Conferences 552 (2024): 01025. http://dx.doi.org/10.1051/e3sconf/202455201025.

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Shell & Tube Heat Exchangers (STHEs) are a critical component for various industrial applications such as chemical, oil & gas, power, etc. Due to their complex design and high manufacturing cost, the efficient operation and optimum design are quite important for overall cost minimization. Multivariate Analysis (MVA) is a technique used for analysing data with more than one type of measurement. In this paper, MVA of STHEs is carried out using Principal Component Analysis (PCA). 12 variables which predicts the Thermo-Hydraulic Performance & the costs for STHEs are considered. In tota

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Volodko, O. S., N. A. Buryak, and A. V. Dergunov. "Analysis of meteorological data of the reanalysis model NCEP GFS for the atmosphere of Krasnoyarsk city." IOP Conference Series: Earth and Environmental Science 1229, no. 1 (2023): 012040. http://dx.doi.org/10.1088/1755-1315/1229/1/012040.

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Abstract Air pollution is an important problem for cities. Krasnoyarsk city is one of the dirtiest cities in the Russian Federation. Meteorological conditions have a significant impact on air pollution. In the present study, for constructing a regression model of forecasting periods of high levels of air pollution, the dimension of meteorological data of the global atmospheric model National Centers for Environmental Prediction Global Forecast System (NCEP GFS) was reduced. The meteorological data were collected between June 2019 and March 2022. To reduce the dimension of meteorological data w

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Gwelo, Abubakari S. "PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM." Oradea Journal of Business and Economics 4, no. 1 (2019): 79–91. http://dx.doi.org/10.47535/1991ojbe062.

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The impact of ignoring collinearity among predictors is well documented in a statistical literature. An attempt has been made in this study to document application of Principal components as remedial solution to this problem. Using a sample of six hundred participants, linear regression model was fitted and collinearity between predictors was detected using Variance Inflation Factor (VIF). After confirming the existence of high relationship between independent variables, the principal components was utilized to find the possible linear combination of variables that can produce large variance w

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Escabias, Manuel, Ana M. Aguilera, and Christian Acal. "logitFD: an R package for functional principal component logit regression." R Journal 14, no. 3 (2022): 231–48. http://dx.doi.org/10.32614/rj-2022-053.

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46

Arum, Kingsley C., Samuel Chidera Ndukwe, Henrietta Ebele Oranye, and Omeiza Bashiru Sule. "COMPARATIVE ANALYSIS OF RIDGE AND PRINCIPAL COMPONENT REGRESSION IN ADDRESSING MULTICOLLINEARITY." FUDMA JOURNAL OF SCIENCES 9, no. 1 (2025): 240–45. https://doi.org/10.33003/fjs-2025-0901-2981.

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Multicollinearity arises when two or more regressors are correlated in multiple linear regression model (MLRM) and in most cases, one regressor variable can be predicted from another. Multicollinearity majorly results in inefficient regression model estimates and poor performance of the regression model. However, multicollinearity problem can easily be handled using various methods such as ridge regression, lasso regression, principal components regression, etc. This study compared the effectiveness of two estimators in handling multicollinearity problem in a given dataset. The estimators bein

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Yunia, Annisa Alma, Dianne Amor Kusuma, Bambang Suhandi, and Budi Nurani Ruchjana. "Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi." Desimal: Jurnal Matematika 3, no. 3 (2020): 211–18. http://dx.doi.org/10.24042/djm.v3i3.6108.

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Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aimed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited se

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48

Wang, Duo, and Toshihisa Tanaka. "Kernel Principal Component Analysis Allowing Sparse Representation and Sample Selection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 1 (2019): 9–20. http://dx.doi.org/10.37936/ecti-cit.2019131.187506.

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Kernel principal component analysis (KPCA) is a kernelized version of principal component analysis (PCA). A kernel principal component is a superposition of kernel functions. Due to the number of kernel functions equals the number of samples, each component is not a sparse representation. Our purpose is to sparsify coefficients expressing in linear combination of kernel functions, two types of sparse kernel principal component are proposed in this paper. The method for solving sparse problem comprises two steps: (a) we start with the Pythagorean theorem and derive an explicit regression expres

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Horváth, József, and Sándor Kovács. "The Examination of the Effects of Value Modifying Factors on Dairy Farms." Acta Agraria Debreceniensis, no. 24 (October 11, 2006): 36–40. http://dx.doi.org/10.34101/actaagrar/24/3222.

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We wish to present a method to quantify the value modifying effects when comparing animal farms. To achieve our objective, multi-variable statistical methods were needed. We used a principal component analysis to originate three separate principal components from nine variables that determine the value of farms. A cluster analysis was carried out in order to classify farms as poor, average and excellent. The question may arise as to which principal components and which variables determine this classification.After pointing out the significance of variables and principal components in determini

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Blanco, M., J. Coello, H. Iturriaga, S. Maspoch, and M. Redon. "Principal Component Regression for Mixture Resolution in Control Analysis by UV-Visible Spectrophotometry." Applied Spectroscopy 48, no. 1 (1994): 37–43. http://dx.doi.org/10.1366/0003702944027633.

Full text

Abstract:

The potential of principal component regression (PCR) for mixture resolution by UV-visible spectrophotometry was assessed. For this purpose, a set of binary mixtures with Gaussian bands was simulated, and the influence of spectral overlap on the precision of quantification was studied. Likewise, the results obtained in the resolution of a mixture of components with extensively overlapped spectra were investigated in terms of spectral noise and the criterion used to select the optimal number of principal components. The model was validated by cross-validation, and the number of significant prin

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