Relevant bibliographies by topics / Principal component regression and principal component analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Principal component regression and principal component analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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

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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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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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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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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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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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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 (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
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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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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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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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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 variables. Two new analyses, decompositions into finer components and fitting higher order structures, are presented next, followed by an illustration of CPCA on contingency tables, and CPCA of residuals that includes assessing reliability using the bootstrap method.
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Dissertations / Theses on the topic "Principal component regression and principal component analysis"

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Fučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by the industry sector to which the individual stocks belong. Joining together PCA with classical linear regression creates principal components regression which is further in the thesis applied to the German DAX 30 index logarithmic returns forecasting using various macroeconomic and financial predictors. The correlation between two energy stocks returns - Chevron and ExxonMobil is forecasted using orthogonal (or PCA) GARCH. The constructed forecast is then compared with the predictions constructed by the conventional multivariate volatility models - EWMA and DCC GARCH.
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Wang, Guoshen. "Analysis of Additive Risk Model with High Dimensional Covariates Using Correlation Principal Component Regression." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/51.

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One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patients¡¯ survival based on their gene expression data. Applying semeparametric additive risk model of survival analysis, this thesis proposes a new approach to conduct the analysis of gene expression data with the focus on model¡¯s predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. Also, we employ the time dependent AUC and RMSEP to assess how well the model predicts the survival time. Furthermore, the proposed method is able to identify significant genes which are related to the disease. Finally, this proposed approach is illustrated by simulation data set, the diffuse large B-cell lymphoma (DLBCL) data set, and breast cancer data set. The results show that the model fits both of the data sets very well.
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Jales, Juliana Viana. "A ImportÃncia da GestÃo no Desenvolvimento Municipal do Estado do CearÃ, 2009 a 2012." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=17144.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior<br>The role of Brazilian cities had great changes over the last twenty-six. The process of decentralization allowed with the 1988 Constitution aimed the financial and political strengthening of the states and especially the municipalities in relation to the federal government. Thus, given the cities greater autonomy, as became 'federative'. Areas such as health and education have gained proper attribution of the local governments and increase the administrative burden, thereby increasing the financial and cities now have to raise more to fulfill them. The administration of a municipality is what organizes and prepares it for development. The need for a management organized so that the population is well served in their rights converge for the concept of sustainable development. Whereas a municipality depends on many factors to develop sustainably, that is, given its population and thinking of the future generations, we need studies that seek to analyze the situation of the municipalities in its various aspects in order to find answers or directions to removal or replacement of obstructing the process. As well as for the development to occur in one place, one needs appropriate to their stimulus policies, the government also needs favorable framework conditions for public policies of local development induction be effective. Thus, the aim of this study was to analyze whether the municipal administrations of the state of Cearà contributed, from 2009 to 2012 for the development and sustainability of their municipalities. A Municipal Management index was constructed (IGM) for 2009 and 2012 through structure indicators of the management of municipalities. Through the use of analysis techniques of Principal Components was calculated Municipal Sustainability Index (ISM), and the same variables was made by the group of municipalities in seven clusters. To see the relationship between municipal management and sustainability, we used the cumulative distribution method by quantile regression. Variables such as spending on health, spending on education and the municipal debt proved to be very important for the sustainability of cities. It was concluded that for most municipalities there was no change in sustainability over the three years, though there have been changes of management, however the values decreased, which implies that some of the characteristics of the municipalities at the beginning of his administration in 2009 may have It has been lost, such as stocks and services that are no longer running and available to the population, councils who failed to act, municipal plans that no longer exist, joints that were undone. It was noticed that the management has a direct impact on sustainability, being instrumental in the development of the municipality, in all aspects, but mainly by the variables used here to generate adequate living conditions for its residents.<br>O papel dos municÃpios brasileiros sofreu grandes mudanÃas nos Ãltimos vinte e seis anos. O processo de descentralizaÃÃo permitido com a ConstituiÃÃo de 1988 teve por objetivo o fortalecimento financeiro e polÃtico dos estados e, principalmente, dos municÃpios em relaÃÃo ao governo federal. Com isso, conferiu aos municÃpios maior autonomia, pois passaram a ser âentes federativosâ. Ãreas como saÃde e educaÃÃo ganharam atribuiÃÃes prÃprias dos municÃpios e se aumentam as obrigaÃÃes administrativas, consequentemente, aumentam as financeiras e os municÃpios passaram a ter que arrecadar mais para cumprÃ-las. A administraÃÃo de um municÃpio à o que o organiza e o prepara para o desenvolvimento. A necessidade de uma gestÃo organizada para que a populaÃÃo seja bem atendida em seus direitos converge, entÃo, para este conceito de desenvolvimento sustentÃvel. Considerando que um municÃpio depende de muitos fatores para se desenvolver com sustentabilidade, ou seja, atendendo sua populaÃÃo e pensando nas geraÃÃes futuras, sÃo necessÃrios estudos que procuram analisar a situaÃÃo dos municÃpios em seus mais variados aspectos para que se encontrem respostas ou direcionamentos para a remoÃÃo ou substituiÃÃo dos entraves ao processo. Assim como, para que ocorra o desenvolvimento em um local, precisa-se de polÃticas adequadas ao seu estÃmulo, o poder pÃblico tambÃm necessita de condiÃÃes estruturais favorÃveis para que as polÃticas pÃblicas sejam efetivas. Dessa forma, o objetivo geral deste estudo foi analisar se as gestÃes municipais do estado do Cearà contribuÃram, no perÃodo de 2009 a 2012, para o desenvolvimento com sustentabilidade dos seus municÃpios. Foi calculado um Ãndice de GestÃo Municipal (IGM) para 2009 e 2012 atravÃs de indicadores de estrutura da gestÃo dos municÃpios. AtravÃs do uso das tÃcnicas de AnÃlise dos Componentes Principais foi calculado o Ãndice de Sustentabilidade Municipal (ISM), e com as mesmas variÃveis fez-se o agrupamento dos municÃpios em sete clusters. Para ver as relaÃÃes entre a gestÃo municipal e a sustentabilidade, utilizou-se o mÃtodo de distribuiÃÃo cumulativa, atravÃs da RegressÃo QuantÃlica. VariÃveis como despesa com saÃde, despesa com educaÃÃo e o endividamento municipal mostraram-se muito importantes para a sustentabilidade dos municÃpios. Concluiu-se que para a maior parte dos municÃpios nÃo houve mudanÃa na sustentabilidade ao longo dos trÃs anos, porÃm houveram mudanÃas de gestÃo, entretanto os valores diminuÃram, o que pressupÃe que algumas das caracterÃsticas dos municÃpios no inÃcio de sua gestÃo em 2009 podem ter sido perdidas, como aÃÃes e serviÃos que deixaram de ser executados e oferecidos à populaÃÃo, conselhos que deixaram de atuar, planos municipais que deixaram de existir, articulaÃÃes que foram desfeitas. Percebeu-se que a gestÃo tem impacto direto sobre a sustentabilidade, sendo peÃa fundamental no desenvolvimento do municÃpio, em todos os aspectos, mas principalmente, pelas variÃveis utilizadas aqui, para gerar condiÃÃes de vida adequadas para seus habitantes.
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Kallur, Ravi Lochan. "Novel approach to predict the crash testing data using multiple regression analysis and principal component analysis." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Zuzarte, Ian Jeromino. "A Principal Component Regression Analysis for Detection of the Onset of Nocturnal Hypoglycemia in Type 1 Diabetic Patients." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1226955083.

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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.
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Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able to determine the risk profile. The analysis is based on survey data from respondents living in Sweden. The main findings are that variables such as income, consumption rate, experience of a financial bear market, and various psychometric variables are significant in determining a financial risk profile.<br>I samband med en ökad automatiseringstrend har digital investeringsrådgivning dykt upp som ett nytt fenomen. Av central betydelse är tjänstens förmåga att bedöma en investerares förmåga till att bära finansiell risk. Logistik regression tillämpas för att bedöma en icke- professionell investerares vilja att bära finansiell risk. Målet med uppsatsen är således att identifiera ett antal faktorer med signifikant förmåga till att bedöma en icke-professionell investerares riskprofil. Med andra ord, så syftar denna uppsats till att studera förmågan hos ett antal socioekonomiska- och psykometriska variabler. För att därigenom utveckla en prediktiv modell som kan skatta en individs finansiella riskprofil. Analysen genomförs med hjälp av en enkätstudie hos respondenter bosatta i Sverige. Den huvudsakliga slutsatsen är att en individs inkomst, konsumtionstakt, tidigare erfarenheter av abnorma marknadsförhållanden, och diverse psykometriska komponenter besitter en betydande förmåga till att avgöra en individs finansiella risktolerans
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Zuzarte, Ian. "A principal component regression analysis for detection of the onset of nocturnal hypoglycemia in Type I diabetic patients." Akron, OH : University of Akron, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1226955083.

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Thesis (M.S.)--University of Akron, Dept. of Biomedical Engineering, 2008.<br>"December, 2008." Title from electronic thesis title page (viewed 12/12/2009) Advisor, Dale H. Mugler; Committee members, Daniel B. Sheffer, Bruce C. Taylor; Department Chair, Daniel B. Sheffer; Dean of the College, George K. Haritos; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.
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Amrani, Naoufal, Joan Serra-Sagrista, Miguel Hernandez-Cabronero, and Michael Marcellin. "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data." IEEE, 2016. http://hdl.handle.net/10150/623190.

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Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.
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Maxwell, Kori Lloyd Hugh. "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/67.

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Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.
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Books on the topic "Principal component regression and principal component analysis"

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Zhang, Wenfei. Regression based principal component analysis for sparse functional data with applications to screening pubertal growth paths. [publisher not identified], 2012.

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Jolliffe, I. T. Principal Component Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8.

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Jolliffe, I. T. Principal component analysis. 2nd ed. Springer, 2010.

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Vidal, René, Yi Ma, and S. S. Sastry. Generalized Principal Component Analysis. Springer New York, 2016. http://dx.doi.org/10.1007/978-0-387-87811-9.

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

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Naik, Ganesh R., ed. Advances in Principal Component Analysis. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6704-4.

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Sanguansat, Parinya. Principal component analysis - multidisciplinary applications. InTech, 2012.

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Hyvarinen, Aapo. Independent component analysis. J. Wiley, 2001.

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Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. J. Wiley, 2001.

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Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2915-8.

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Book chapters on the topic "Principal component regression and principal component analysis"

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Jolliffe, I. T. "Principal Components in Regression Analysis." In Principal Component Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8_8.

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Deshmukh, Rushali A., Prachi Jadhav, Sakshi Shelar, Ujwal Nikam, Dhanshri Patil, and Rohan Jawale. "Stock Price Prediction Using Principal Component Analysis and Linear Regression." In Emerging Technologies in Data Mining and Information Security. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_28.

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Guoqi, Long. "Research on Economic Growth Based on MCMC Principal Component Regression Analysis." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62743-0_98.

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Agarwal, Neetu, Susmita Ray, and K. C. Tripathi. "Enhancing Cotton Crop Yield Prediction Through Principal Component Analysis and Regression Modelling." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3588-4_20.

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Kozłowski, Edward, Dariusz Mazurkiewicz, Jarosław Sȩp, and Tomasz Żabiński. "The Use of Principal Component Analysis and Logistic Regression for Cutter State Identification." In Innovations in Industrial Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78170-5_34.

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Arakali, V. S. "Application of Ridge Regression and Principal Component Analysis to Specialty Glass Data for Encapsulation of Nuclear Waste." In Risk Analysis. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0730-1_8.

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Ravi, Vadlamani, and Ranabir De. "Principal Component Analysis and General Regression Auto Associative Neural Network Hybrid as One-Class Classifier." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_15.

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Bonham-Carter, G. F., and E. C. Grunsky. "Two Ideas for Analysis of Multivariate Geochemical Survey Data: Proximity Regression and Principal Component Residuals." In Handbook of Mathematical Geosciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78999-6_23.

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Cajilima, Wilson, and Pablo Arévalo. "Multi-Method Spectral Predictive Models with FTIR-ATR in the Simultaneous Quantification of Ethanol and Legal Methanol Limits in Ecuadorian Clear Spirits." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_7.

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Abstract In this research, the feasibility of employing multiple machine learning algorithms with FTIR-ATR spectroscopy for the simultaneous quantification of ethanol and legal limits of methanol in distilled and artisanal beverages characteristic of Ecuador is investigated, based on spectral matrix similarity. Initially, spectra acquired in the range of 4000 to 400 $${\text{cm}}^{-1}$$ cm - 1 underwent spectral preprocessing including baseline correction, smoothing, normalization, first and second derivative, and their combinations. Forty-eight distinct treatments were used to construct models employing Principal Component Regression (PCR) and Partial Least Squares 2 (PLS2). The treatment yielding superior metrics was employed for constructing an Artificial Neural Network combined with Principal Component Analysis (PCA-ANN) and Recursive Feature Elimination (RFE-ANN) utilizing a Decision Tree Regressor as a variable selector. Based on confidence intervals and hypothesis testing of statistics such as root mean squared error of prediction (RMSEP) the PCR and PLS2 models exhibited superior performance. PCR achieved detection and quantification limits of 0.25% and 0.7%, respectively. In commercial beverages, predictions were compared with results obtained via gas chromatography, where again PCR and PLS2 demonstrated the finest metrics, showcasing speed and high cost-effectiveness, rendering them viables alternatives for preliminary quality control analysis.
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Bellemans, Aurélie, Mohammad Rafi Malik, Fabrizio Bisetti, and Alessandro Parente. "A Machine-Learning Framework for Plasma-Assisted Combustion Using Principal Component Analysis and Gaussian Process Regression." In Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80542-5_23.

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Conference papers on the topic "Principal component regression and principal component analysis"

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Wicaksono, Pramaditya, Setiawan Djody Harahap, Muhammad Kamal, and Candra S. D. Kartika. "Comparison of corrected surface reflectance, principal component analysis, and kernel principal component analysis for seagrass percent cover mapping using Sentinel-2 based on various regression models." In Active and Passive Remote Sensing of Oceans, Seas, and Lakes, edited by Kuo-Hsin Tseng, Robert J. Frouin, Jong-Kuk Choi, and Hiroshi Murakami. SPIE, 2025. https://doi.org/10.1117/12.3038301.

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Salian, Shravya, S. Cherishma, and Omkar S. Powar. "Enhanced Brain Tumor Detection using Support Vector Classifier and Logistic Regression with Principal Component Analysis." In 2024 Control Instrumentation System Conference (CISCON). IEEE, 2024. http://dx.doi.org/10.1109/ciscon62171.2024.10696269.

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Routh, Bikky, Vikram Kumawat, Arijit Guha, Siddhartha Mukhopadhyay, and Amit Patra. "State-of-Health Estimation of Li-ion Batteries using Multiple Linear Regression and Optimized Feature Extraction based on Principal Component Analysis." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10626868.

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Bhalla, Akshit. "Reverse Principal Component Analysis for Multi-Output Regression." In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). IEEE, 2020. http://dx.doi.org/10.1109/accthpa49271.2020.9213193.

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Maenaka, T., K. Honda, and H. Ichihashi. "Local Independent Component Analysis with Fuzzy Clustering and Regression-principal Component Analysis." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681811.

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Chen, Ming-ming, and Jing-lian Ma. "Application of Principal Component Regression Analysis in Economic Analysis." In 3rd International Conference on Management Science, Education Technology, Arts, Social Science and Economics. Atlantis Press, 2015. http://dx.doi.org/10.2991/msetasse-15.2015.255.

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Arai, Rei, Taichi Nishiyama, Naoki Nakatani, and Taketoshi Okuno. "Spectrophotometric Determination of Nutrients Using Principal Component Regression." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-79304.

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Certain environmental factors such as salinity, dissolved oxygen and chlorophyll concentration can be measured by electric or optical sensors, enabling continuous and automatic measurement with high resolution in time and space. Such measurement is of great importance in the monitoring of marine environments. In order to understand the ecosystem of the sea in detail, the distribution of and changes in nutrient concentrations should be measured in terms of primary production. Generally, since seawater contains high concentrations of chloride, bromide and so on, nutrients must be extracted from these ions. Automatic measurement has therefore proven difficult, and water sampling and chemical analysis in a laboratory are required. This paper proposes a simplified method for measuring nitroxide concentration in seawater using ultraviolet spectrometry and principal component regression (PCR). The present method is shown to be superior to the conventional one since it does not require chemical processing or filtration of seawater.
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Yuling, Yang, Guo Xinliang, Cui Zhaolun, et al. "Quantitative infrared spectrum detection of SF6 decomposition components based on principal component regression analysis." In 2017 China International Electrical and Energy Conference (CIEEC). IEEE, 2017. http://dx.doi.org/10.1109/cieec.2017.8388552.

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Rocha, Humberto, Wu Li, and Andrew Hahn. "Principal Component Regression for Fitting Wing Weight Data of Subsonic Transports." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7052.

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Young, Richard A. "Principal component analysis of macaque LGN cell chromatic data." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.tuh4.

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The spectral curves of LGN cells from a famous study1 were subjected to principal component analysis to determine the minimal set of basis vectors needed to describe the data. Over 92% of the variability in the 441 spectral curves (147 cells measured at three intensity levels) could be described by a unique orthonormal set of three basis vectors. Every cell’s spectral curve could be represented as a linear combination of these vectors, with minimal loss of information. An orthogonal rotation of the vectors, which preserves all Euclidean chromatic distances in the color space spanned by the vectors, led to a reasonable correspondence to psychophysical chromatic basis vectors.2 Possible cone input types were suggested by projecting the components and raw spectral curves into tricone space using multiple regression techniques.
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Reports on the topic "Principal component regression and principal component analysis"

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Krishnaiah, P. R., and S. Sarkar. Principal Component Analysis Under Correlated Multivariate Regression Equations Model. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160266.

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Iurasova, Olga, Larysa Ivashko, Oleksandr Maksymov, and Julia Maksymova. Impact of Return on Education on Economic Growth in EU Countries. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.2.6.

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The objective of this article is to assess the impact of returns on the education and professional skills of workers on economic growth in EU countries. Based on open data, two principal components were formed to identify the aggregated influence of selected indicators on GDP growth. These principal components allow for the evaluation of the degree of influence of education and professional skills of workers on GDP growth for each country. Countries were clustered according to the degree of influence of the obtained principal components on the level of economic development using the k-means method. As a result, 20 EU countries were divided into three clusters. The first cluster consists of developed countries with a high share of innovations. The sec-ond cluster includes developed countries with a lower share of innovations than the first cluster and moderate values of the return on education. The third cluster includes countries with a large share of non-innovative sec-tors. Subsequently, a regression model was constructed to analyse the influence of each component on GDP growth. Based on the comparison of coefficient values, it was concluded that the increase in the relationship between the level of education and GDP growth in EU countries is observed with the increase in the innovative and technological components of the economy.
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MARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/810934.

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Hamilton, James, and Jin Xi. Principal Component Analysis for Nonstationary Series. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32068.

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Aït-Sahalia, Yacine, and Dacheng Xiu. Principal Component Analysis of High Frequency Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21584.

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สิริภัทราวรรณ, อุบลรัตน์, та สุวัสสา พงษ์อำไพ. การประเมินอายุการเก็บแบบรวดเร็วของผลิตภัณฑ์อาหารแปรรูปโดยใช้ NIR specytoscopy และ Chemometrics : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2014. https://doi.org/10.58837/chula.res.2014.58.

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งานวิจัยนี้พัฒนาการประเมินคุณภาพและอายุการเก็บของผลิตภัณฑ์อาหารแปรรูปพร้อมบริโภค ด้วยวิธี แบบรวดเร็วโดยใช้ NIR spectroscopy ร่วมกับ chemometrics ทำโดยเตรียมผลิตภัณฑ์ไส้กรอกหมูบรรจุใน ถุงพลาสติกภายใต้ภาวะสุญญากาศและเก็บรักษาที่อุณหภูมิ 4 °C ติดตามการเปลี่ยนแปลงคุณภาพทางเคมี (ค่า pH) ทางกายภาพ (ค่าแรงตัดขาด และ ค่าสี) ทางจุลินทรีย์ (จำนวนแบคทีเรียทั้ง หมด และ แบคทีเรียแลกติก) และทาง ประสาทสัมผัส (odor, color, appearance และ overall acceptability) ของผลิตภัณฑ์ในระหว่างการเก็บรักษา รวมทั้งวิเคราะห์โดยใช้ near infrared spectroscopy จากผลการทดลองพบว่า ผลิตภัณฑ์มีค่า pH ค่าแรงตัดขาด ค่าสี (L* (ความเข้ม-สว่าง), a* (เขียว-แดง) และ b* (น้ำเงิน-เหลือง)) ลดลง เมื่อระยะเวลาการเก็บเพิ่มขึ้น จากการวัดการเปลี่ยนแปลงคุณภาพทางจุลินทรีย์ของผลิตภัณฑ์ พบว่า จำนวนแบคทีเรียทั้ง หมด เพิ่มขึ้น เมื่อ ระยะเวลาการเก็บรักษาเพิ่มขึ้น เมื่อเก็บรักษาผลิตภัณฑ์ไว้นาน 8 วัน จำนวนแบคทีเรียแลกติกจึงเพิ่มสูงขึ้น และเมื่อ เก็บรักษานาน 16 วัน พบว่าการเพิ่มของจำนวนจุลินทรีย์ส่งผลให้ ค่าแรงตัดขาด ค่าสี ลดลง นอกจากนี้ยังพบว่าค่า pH ที่ลดลงเป็นผลมาจากปริมาณ แบคทีเรียแลกติกที่เพิ่มขึ้น ซึ่งส่งผลต่อการยอมรับด้านประสาทสัมผัสของผู้บริโภค จากการประเมินคุณภาพทางประสาทสัมผัสของผลิตภัณฑ์ พบว่า เมื่อระยะเวลาการเก็บเพิ่มขึ้นตัวอย่างมีการเปลี่ยนแปลงของกลิ่น (off-odor) ลักษณะปรากฏ (slimy appearance) และสี (off-color) เพิ่มมากขึ้น ส่วนค่าการ ยอมรับโดยรวม (overall acceptability) มีคะแนนลดลง โดยการเปลี่ยนแปลงดังกล่าวเพิ่มมากขึ้น เมื่อระยะเวลาการ เก็บนานขึ้น ซึ่งเป็ นผลมาจากการเจริญของจุลินทรีย์ที่เพิ่มขึ้น ในระหว่างการเก็บรักษา และพบว่าผลคะแนนทาง ประสาทสัมผัสสอดคล้องกับค่าการเปลี่ยนแปลงทางเคมี และทางกายภาพของผลิตภัณฑ์ สำหรับการวัดการเปลี่ยนแปลงคุณภาพผลิตภัณฑ์ระหว่างการเก็บรักษาด้วย NIR spectroscopy โดยวัดค่า reflectance ในช่วงความยาวคลื่น 400-1000 nm ปรับแต่งสเปคตรัมด้วยวิธี Savitzky-Golay 2nd derivatives ซึ่ง เป็นวิธีที่เหมาะสม จากนั้น วิเคราะห์ข้อมูลด้วย chemometrics โดยใช้ principal component analysis (PCA) ใน การลดจำนวนข้อมูล และจำแนกตัวอย่างที่มีคุณภาพแตกต่างกัน พบว่าการใช้ NIR spectroscopy ร่วมกับ PCA สามารถจัดจำแนกตัวอย่างเป็นกลุ่ม (clusters) ที่มีคุณภาพแตกต่างกันตามระยะเวลาการเก็บรักษาได้ การทำนาย จำนวนโคโลนีของแบคทีเรียทั้งหมดที่เจริญบนผลิตภัณฑ์จากค่า reflectance ที่ได้จาก NIR spectroscopy ทำโดยใช้ partial least square regression (PLSR) พบว่าการใช้ PLSR ให้ค่า coefficient of determination (R2) เป็น 0.85 และ 0.81 สำหรับการ calibration และ validation ตามลำดับ และค่า root mean square error of calibration (RMSEC) และ root mean square error of validation (RMSEV) เป็น 1.88 และ 1.22 Log (CFU/g) ตามลำดับ จากผลการวิเคราะห์จะเห็นได้ว่า NIR spectroscopy ร่วมกับ PLSR สามารถใช้ในการ ทำนายการเจริญของแบคทีเรียทัง้ หมดที่เจริญบนผลิตภัณฑ์ และมีความถูกต้องสูง
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7

Eick, Brian, Zachary Treece, Billie Spencer, et al. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), 2018. http://dx.doi.org/10.21079/11681/27365.

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Federer, W. T., C. E. McCulloch, and J. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis Using SYSTAT/FACTOR. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada184920.

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Federer, W. T., C. E. McCulloch, and N. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis using BMDP/4M. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada185179.

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Thompson, David C., Janine C. Bennett, Diana C. Roe, and Philippe Pierre Pebay. Scalable multi-correlative statistics and principal component analysis with Titan. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/984172.

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