Relevant bibliographies by topics / Multicollinearity

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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 'Multicollinearity'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Multicollinearity"

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Tsagris, Michail, and Nikolaos Pandis. "Multicollinearity." American Journal of Orthodontics and Dentofacial Orthopedics 159, no. 5 (May 2021): 695–96. http://dx.doi.org/10.1016/j.ajodo.2021.02.005.

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Alin, Aylin. "Multicollinearity." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (March 8, 2010): 370–74. http://dx.doi.org/10.1002/wics.84.

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Ohyver, Margaretha, and Herena Pudjihastuti. "Pemodelan Tingkat Penghunian Kamar Hotel di Kendari dengan Transformasi Wavelet Kontinu dan Partial Least Squares." ComTech: Computer, Mathematics and Engineering Applications 5, no. 2 (December 1, 2014): 1178. http://dx.doi.org/10.21512/comtech.v5i2.2435.

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Multicollinearity and outliers are the common problems when estimating regression model. Multicollinearitiy occurs when there are high correlations among predictor variables, leading to difficulties in separating the effects of each independent variable on the response variable. While, if outliers are present in the data to be analyzed, then the assumption of normality in the regression will be violated and the results of the analysis may be incorrect or misleading. Both of these cases occurred in the data on room occupancy rate of hotels in Kendari. The purpose of this study is to find a model for the data that is free of multicollinearity and outliers and to determine the factors that affect the level of room occupancy hotels in Kendari. The method used is Continuous Wavelet Transformation and Partial Least Squares. The result of this research is a regression model that is free of multicollinearity and a pattern of data that resolved the present of outliers.
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Higgins, J., and J. Gruber. "Multicollinearity and Biased Estimation." Statistician 35, no. 3 (1986): 401. http://dx.doi.org/10.2307/2987767.

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Jurczyk, Tomáš. "Outlier detection under multicollinearity." Journal of Statistical Computation and Simulation 82, no. 2 (February 2012): 261–78. http://dx.doi.org/10.1080/00949655.2011.638634.

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Kelava, Augustin, Helfried Moosbrugger, Polina Dimitruk, and Karin Schermelleh-Engel. "Multicollinearity and Missing Constraints." Methodology 4, no. 2 (January 2008): 51–66. http://dx.doi.org/10.1027/1614-2241.4.2.51.

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Multicollinearity complicates the simultaneous estimation of interaction and quadratic effects in structural equation modeling (SEM). So far, approaches developed within the Kenny-Judd (1984 ) tradition have failed to specify additional and necessary constraints on the measurement error covariances of the nonlinear indicators. Given that the constraints comprise, in part, latent linear predictor correlations, multicollinearity poses a problem for such approaches. Klein and Moosbrugger’s (2000 ) latent moderated structural equations approach (LMS) approach does not utilize nonlinear indicators and should therefore not be affected by this problem. In the context of a simulation study, we varied predictor correlation and the number of nonlinear effects in order to compare the performance of three approaches developed for the estimation of simultaneous nonlinear effects: Ping’s (1996 ) two-step approach, a correctly extended Jöreskog-Yang (1996 ) approach, and LMS. Results show that in contrast to the Jöreskog-Yang approach and LMS, the two-step approach produces biased parameter estimates and false inferences under heightened multicollinearity. Ping’s approach resulted in overestimated interaction effects and underestimated quadratic effects.
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Öztürk, Fikri, and Fikri Akdeniz. "Ill-conditioning and multicollinearity." Linear Algebra and its Applications 321, no. 1-3 (December 2000): 295–305. http://dx.doi.org/10.1016/s0024-3795(00)00147-6.

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Winship, Christopher, and Bruce Western. "Multicollinearity and Model Misspecification." Sociological Science 3 (2016): 627–49. http://dx.doi.org/10.15195/v3.a27.

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Daoud, Jamal I. "Multicollinearity and Regression Analysis." Journal of Physics: Conference Series 949 (December 2017): 012009. http://dx.doi.org/10.1088/1742-6596/949/1/012009.

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GILBERT, C. L. "THE DIAGNOSIS OF MULTICOLLINEARITY*." Oxford Bulletin of Economics and Statistics 40, no. 2 (May 1, 2009): 87–91. http://dx.doi.org/10.1111/j.1468-0084.1978.mp40002001.x.

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Dissertations / Theses on the topic "Multicollinearity"

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Clark, Patrick Carl Jr. "The Effects of Multicollinearity in Multilevel Models." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1375956788.

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Duxbury, Scott W. "Diagnosing Multicollinearity in Exponential Random Graph Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491393848069144.

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Gou, Zhenkun. "Canonical correlation analysis and artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269409.

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Månsson, Kristofer. "Issues of multicollinearity and conditional heteroscedasticy in time series econometrics." Doctoral thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-31977.

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This doctoral thesis consists of four chapters all related to the field of time series econometrics. The main contribution is firstly the development of robust methods when testing for Granger causality in the presence of generalized autoregressive conditional heteroscedasticity (GARCH) and causality-in-variance (i.e. spillover) effects. The second contribution is the development of different shrinkage estimators for count data models which may be used when the explanatory variables are highly inter-correlated. The first essay investigated the effect of spillover on some tests for causality in a Granger sense. As a remedy to the problem of over-rejection caused by the spillover effects White’s heteroscedasticity consistent covariance matrix is proposed. In the second essay the effect of GARCH errors on the statistical tests for Granger causality is investigated. Here some wavelet denoising methods are proposed and by means of Monte Carlo simulations it is shown that the size properties of the tests based on wavelet filtered data is better than the ones based on raw data. In the third and fourth essays ridge regression estimators for the Poisson and negative binomial (NB) regression models are investigated respectively. Then finally in the fifth essaya Liu type of estimator is proposed for the NB regression model. By using Monte Carlo simulations it is shown that the estimated MSE is lower for the ridge and Liu type of estimators than maximum likelihood (ML).
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Moineddin, Rahim. "Comments on Mallow's C¦p statistics and multicollinearity effects on predictions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58663.pdf.

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Bakshi, Girish. "Comparison of ridge regression and neural networks in modeling multicollinear data." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178815205.

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Albarracin, Orlando Yesid Esparza. "Generalized autoregressive and moving average models: control charts, multicollinearity, and a new modified model." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21112017-184544/.

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Recently, in the health surveillance area, control charts have been proposed to decide if the morbidity or mortality of a specific disease reached an epidemic level. This thesis is composed by 3 papers. In the first two papers, CUSUM and EWMA control charts were proposed to monitor count time series with seasonal and trend effects using the Generalized Autoregressive and Moving Average models (GARMA), instead of the independent Generalized Linear Model (GLM) as it is usually used in practice. Different statistics based on transformations, for variables that follow a Negative Binomial distribution, were used in these control charts. In the second paper, two new statistics were proposed based on the ratio of log-likelihood function. Different scenarios describing disease profiles were considered to evaluate the effect of omission of serial correlation in EWMA and CUSUM control charts. The performance of CUSUM and EWMA charts when the serial correlation is neglected in the regression model was measure in terms of average run length (ARL). In summary, when the autocorrelation is neglected, fitting a pure GLM instead of a GARMA model will lead to an increase of false alarms. However, no statistics among the tested ones seem to be robust, in a sense to produce the smallest increase of false alarms in all scenarios. In general, all monitored statistics presented a smaller ARL_0 for higher values of autocorrelation. \\\\ In the last paper, the GARMA models (p, q) with p and q simultaneously different from zero were studied since that two features were observed in practice. One is the multicollinearity, which may lead to a non-convergence of the maximum likelihood, using iteratively reweighted least squares. The second is the inclusion of the same lagged observations into the autoregressive and moving average components confounding the interpretation of the parameters. In a general sense, simulation studies show that the modified model provide estimators closer to the parameters and offer confidence intervals with higher coverage percentage than obtained with the GARMA model, but some restrictions in the parametric space are imposed to guarantee the stationarity of the process. Also, a real data analysis illustrate the GARMA-M fit for daily hospilatization rates of elderly people due to respiratory diseases from October 2012 to April 2015 in São Paulo city, Brazil.
Recentemente, no campo da saúde, gráficos de controle têm sido propostos para monitorar a morbidade ou a mortalidade decorrentes de doenças. Este trabalho está composto por três artigos. Nos dois primeiros artigos, gráficos de controle CUSUM e EWMA foram propostos para monitorar séries temporais de contagens com efeitos sazonais e de tendência usando os modelos Generalized autoregressive and moving average models (GARMA), em vez dos modelos lineares generalizados (GLM), como usualmente são utilizados na prática. Diferentes estatísticas baseadas em transformações, para variávies que seguem uma distribuição Binomial Negativa, foram usadas nestes gráficos de controle. No segundo artigo foram propostas duas novas estatísticas baseadas na razão da função de log-verossimilhança. Diferentes cenários que descrevem perfis de doenças foram considerados para avaliar o efeito da omissão da correlação serial nesses gráficos de controle. Este impacto foi medido em termos do Average Run Lenght (ARL). Notou-se que a negligência da correlação serial induz um aumento de falsos alarmes. Em geral, todas as estatísticas monitoradas apresentaram menores valores de ARL_0 para maiores valores de autocorrelação. No entanto, nenhuma estatística entre as consideradas mostrou ser mais robusta, no sentido de produzir o menor aumento de falsos alarmes nos cenários considerados. No último artigo, foram estudados os modelos GARMA (p, q) com p e q simultaneamente diferentes de zero, uma vez que duas características foram observadas na prática. A primeira é a presença de multicolinearidade, que induz à não-convergência do método de máxima verossimilhança usando mínimos quadrados ponderados reiterados. A segunda é a inclusão dos mesmos termos defasados nos componentes autorregressivos e de médias móveis. Um modelo modificado, GARMA-M, foi apresentado para lidar com a multicolinearidade e melhorar a interpretação dos parâmetros. Em sentido geral, estudos de simulação mostraram que o modelo modificado fornece estimativas mais próximas dos parâmetros e intervalos de confiança com uma cobertura percentual maior do que a obtida nos modelos GARMA. No entanto, algumas restrições no espaço paramétrico são impostas para garantir a estacionariedade do processo. Por último, uma análise de dados reais ilustra o ajuste do modelo GARMA-M para o número de internações diárias de idosos devido a doenças respiratórias de outubro de 2012 a abril de 2015 na cidade de São Paulo, Brasil.
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CROPPER, JOHN PHILIP. "TREE-RING RESPONSE FUNCTIONS. AN EVALUATION BY MEANS OF SIMULATIONS (DENDROCHRONOLOGY RIDGE REGRESSION, MULTICOLLINEARITY)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187946.

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The problem of determining the response of tree ring width growth to monthly climate is examined in this study. The objective is to document which of the available regression methods are best suited to deciphering the complex link between tree growth variation and climate. Tree-ring response function analysis is used to determine which instrumental climatic variables are best associated with tree-ring width variability. Ideally such a determination would be accomplished, or verified, through detailed physiological monitoring of trees in their natural environment. A statistical approach is required because such biological studies on mature trees are currently too time consuming to perform. The use of lagged climatic data to duplicate a biological, rather than a calendar, year has resulted in an increase in the degree of intercorrelation (multicollinearity) of the independent climate variables. The presence of multicollinearity can greatly affect the sign and magnitude of estimated regression coefficients. Using series of known response, the effectiveness of five different regression methods were objectively assessed in this study. The results from each of the 2000 regressions were compared to the known regression weights and a measure of relative efficiency computed. The results indicate that ridge regression analysis is, on average, four times more efficient (average relative efficiency of 4.57) than unbiased multiple linear regression at producing good coefficient estimates. The results from principal components regression are slight improvements over those from multiple linear regression with an average relative efficiency of 1.45.
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Kuroki, Quispe André Francisco, and Taza Gianella Milagros Soto. "Factores que determinan el comportamiento del volumen de exportación de café peruano con partida 090111 según los años 1980 - 2017." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/628233.

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La presente tesis está enfocada en los factores que explican el volumen de exportación del café dentro del periodo de 1980 a 2017 en base al área de cultivo, el precio promedio y el rendimiento del café. El propósito de esta investigación es la elaboración de un modelo estadístico que permita a los productores del sector de café pronosticar sus volúmenes de exportación, nuestra metodología consiste en realizar una investigación cuantitativa, con un diseño concluyente no experimental y un alcance descriptivo correlacional. Los resultaron sacaron a relucir que el precio promedio no es una variable significativa que afecte al volumen de exportación, el área cultivada y el rendimiento son los factores primordiales que el productor debe cuidar para aumentar su volumen. El rendimiento del café es una variable muy sensible y en esencia su buen manejo lleva a aumentar significativamente el volumen del productor.
The present thesis is focused on the factors that explain the export volume of coffee in the period from 1980 to 2017 based on the area of cultivation, average price and coffee yield. The purpose of this research is the development of a statistical model that allows producers in the coffee sector to forecast their export volumes, our methodology is to conduct a quantitative research, with a conclusive non-experimental design and a correlational descriptive scope. The results showed that the average price is not a significant variable that affects the export volume, the cultivated area and the yield are the main factors that the producer must take care of to increase its volume. The yield of coffee is a very sensitive variable and in essence its good management leads to significantly increase the volume of the producer.
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Gripencrantz, Sarah. "Evaluating the Use of Ridge Regression and Principal Components in Propensity Score Estimators under Multicollinearity." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226924.

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Multicollinearity can be present in the propensity score model when estimating average treatment effects (ATEs). In this thesis, logistic ridge regression (LRR) and principal components logistic regression (PCLR) are evaluated as an alternative to ML estimation of the propensity score model. ATE estimators based on weighting (IPW), matching and stratification are assessed in a Monte Carlo simulation study to evaluate LRR and PCLR. Further, an empirical example of using LRR and PCLR on real data under multicollinearity is provided. Results from the simulation study reveal that under multicollinearity and in small samples, the use of LRR reduces bias in the matching estimator, compared to ML. In large samples PCLR yields lowest bias, and typically was found to have the lowest MSE in all estimators. PCLR matched ML in bias under IPW estimation and in some cases had lower bias. The stratification estimator was heavily biased compared to matching and IPW but both bias and MSE improved as PCLR was applied, and for some cases under LRR. The specification with PCLR in the empirical example was usually most sensitive as a strongly correlated covariate was included in the propensity score model.
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Books on the topic "Multicollinearity"

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Pineda, Octavio Luis. La multicolinealidad en econometría: Diagnóstico y corrección del problema. México, D.F: SITESA, Sistemas Técnicos de Edición, 1992.

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Moineddin, Rahim. Comments on Mallows' Cp statistics and multicollinearity effects on predictions. Ottawa: National Library of Canada, 2001.

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Kalivas, John H. Mathematical analysis of spectral orthogonality. New York: M. Dekker, 1994.

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Das Problem der Multikollinearität in Regressionsanalysen. Frankfurt am Main: P. Lang, 1994.

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Scott Jones, Julie. Learn to Test for Multicollinearity in SPSS With Data From the English Health Survey (Teaching Dataset) (2002). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526485793.

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Scott Jones, Julie. Learn to Test for Multicollinearity in R With Data From the English Health Survey (Teaching Dataset) (2002). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498670.

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Wiesen, Christopher. Learn About Multicollinearity in SPSS With Data From Transparency, Class Bias, and Redistribution: Evidence From the American States Dataset (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499349.

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Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".
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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Neeleman, D. Multicollinearity in linear economic models. Springer, 2013.

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Book chapters on the topic "Multicollinearity"

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Sen, Ashish, and Muni Srivastava. "Multicollinearity." In Springer Texts in Statistics, 218–32. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-4470-7_10.

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Barrie Wetherill, G., P. Duncombe, M. Kenward, J. Köllerström, S. R. Paul, and B. J. Vowden. "Multicollinearity." In Regression Analysis with Applications, 82–107. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4105-2_4.

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Sen, Ashish, and Muni Srivastava. "Multicollinearity." In Springer Texts in Statistics, 218–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-25092-1_10.

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Corlett, Wilfred. "Multicollinearity." In The New Palgrave Dictionary of Economics, 9171–72. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_984.

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Bahovec, Vlasta. "Multicollinearity." In International Encyclopedia of Statistical Science, 869–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_382.

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Corlett, Wilfred. "Multicollinearity." In The New Palgrave Dictionary of Economics, 1–2. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_984-1.

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Asteriou, Dimitrios, and Stephen G. Hall. "Multicollinearity." In Applied Econometrics, 103–16. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1057/978-1-137-41547-9_5.

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Kacapyr, Elia. "Multicollinearity." In Essential Econometric Techniques, 129–36. 3rd ed. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003213758-9.

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Corlett, Wilfred. "Multicollinearity." In Econometrics, 158–59. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20570-7_22.

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Hoffmann, John P. "Collinearity and Multicollinearity." In Linear Regression Models, 187–200. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-10.

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Conference papers on the topic "Multicollinearity"

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Castillo, Flor A., and Carlos M. Villa. "Symbolic regression in multicollinearity problems." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068377.

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Zainodin, H. J., and S. J. Yap. "Overcoming multicollinearity in multiple regression using correlation coefficient." In INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013): Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823947.

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Zahari, Siti Meriam, Mohammad Said Zainol, and Muhammad Iqbal Al-Banna Bin Ismail. "Weighted ridge M-estimator in the presence of multicollinearity." In 2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER). IEEE, 2012. http://dx.doi.org/10.1109/chuser.2012.6504317.

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Gisin, Vladimir B., Boris A. Putko, and Irina Z. Yarygina. "The Multicollinearity Problem in the Fuzzy Linear Regression Model." In 2022 XXV International Conference on Soft Computing and Measurements (SCM). IEEE, 2022. http://dx.doi.org/10.1109/scm55405.2022.9794871.

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Mehra, Prabhav, Rajee Gupta, Abhishek Mahajan, and Veeky Baths. "Multicollinearity Analysis for Cuffless Blood Pressure Estimation Regression Algorithms." In ICBSP '19: 2019 4th International Conference on Biomedical Imaging, Signal Processing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3366174.3366188.

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Song, Peter, and Chuck Kroll. "The Impact of Multicollinearity on Small Sample Hydrologic Regional Regression." In World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)389.

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Wang, Zi-Hao, and Zao-Jian Zou. "Quantifying Multicollinearity in Ship Manoeuvring Modeling by Variance Inflation Factor." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77121.

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Parameter drift is a tricky problem in system identification, and collinearity in the identified model is considered to be its cause in the field of statistical learning. To diminish the parameter drift and identify the model parameters more accurately, a better understanding of the characteristics of collinearity is necessary. System identification is one of the effective modeling methods in the study of ship manoeuvrability. This paper aims at quantifying and analyzing the collinearity in the modeling of ship manoeuvring motion by using Variance Inflation Factor (VIF). By utilizing the multiple datasets including those from zigzag and turning tests and the combinations of manoeuvres, as well as the data processed by D-optimizing design or difference method, the VIF is applied to quantify the severity of collinearity of different model structures. The results show that the selected manoeuvring models have high collinearity under the data of single standard manoeuvre. The pre-processed test data and an appropriate model structure can alleviate the collinearity, hence to diminish the parameter drift. However, the collinearity of the selected models cannot be eliminated. Some suggestions are given for selecting more appropriate training data and mathematical model structures of ship manoeuvring to accurately estimate the model parameters.
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Araveeporn, Autcha, and Choojai Kuharatanachai. "Comparing Penalized Regression Analysis of Logistic Regression Model with Multicollinearity." In the 2019 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3343485.3343487.

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Nong, Jifu. "A neural network based on canonical correlation for multicollinearity diagnosis." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.208.

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Wang, Rongqiao, Kanghe Jiang, Fulei Jing, Dianyin Hu, and Jun Song. "Dominant Damage Factors Determining for Single Crystal Nickel Superalloys Under Cyclic Loading Based on Principal Component Analysis." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42156.

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A critical plane approach in combination with principal component analysis (PCA) for determining dominant damage factors (DDFs) was developed for single crystal nickel superalloys at elevated temperature. Maximum resolved shear stress (RSS), maximum slip rate and other 2 mesoscopic parameters on the critical plane, defined as the preferential slip plane, were selected as damage parameters. Correlation analysis results indicated that there were strong correlations (i.e. multicollinearity) among the selected parameters. To address this issue, PCA was performed to eliminate the effect of multicollinearity and the DDFs were determined as well. Based on the DDFs a life model was proposed and then validated by the fatigue experimental results. Most of the experimental lives are within the factor three of the predicted ones. The life model has a relatively simple form with reliable constants which facilitates the application in industry design.
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Reports on the topic "Multicollinearity"

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Fekedulegn, B. Desta, J. J. Colbert, R. R. ,. Jr Hicks, and Michael E. Schuckers. Coping with Multicollinearity: An Example on Application of Principal Components Regression in Dendroecology. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2002. http://dx.doi.org/10.2737/ne-rp-721.

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Gamba-Santamaria, Santiago, Luis Fernando Melo-Velandia, and Camilo Orozco-Vanegas. What can credit vintages tell us about non-performing loans? Banco de la República de Colombia, February 2021. http://dx.doi.org/10.32468/be.1154.

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Using Colombian credit vintage data, we decompose the non-performing loans into one component that captures the evolution of the payment capacity of borrowers, and other component that captures changes in the credit risk taken by the financial system at the time of loan disbursement. We use intrinsic estimators and penalized regression techniques to overcome the perfect multicollinearity problem that the model entails. We find that these two type of components have evolved differently over time, and that good economic conditions and loose financial conditions improve the payment capacity of borrowers to meet their obligations, and in turn, they tend to coincide with the financial system engaging in riskier loans. Finally, we advocate the use of this methodology as a policy tool that is easy to apply by financial and economic authorities that dispose of a constant flow of credit vintage information. Through it, they will be able to identify the origin of the credit risk materialization and curb the risk taken by the financial system.
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