Results 31 to 40 of about 2,294,043 (337)
Sparse logistic principal components analysis for binary data [PDF]
, 2010 We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua +2 more
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Functional principal components analysis via penalized rank one approximation [PDF]
, 2008 Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways.
Buja, Andreas +2 more
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Principal components analysis of employment in Eastern Europe [PDF]
Panoeconomicus, 2006 For the last decade, the employment structure is one of the fastest changing areas of Eastern Europe. This paper explores the best methodology to compare the employment situations in the countries of this region.
Savić Mirko
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PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM [PDF]
Oradea Journal of Business and Economics, 2019 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.
Abubakari S.Gwelo
doaj
ANOVA bootstrapped principal components analysis for logistic regression
Croatian Review of Economic, Business and Social Statistics, 2022 Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal components is selected to be used in a classification or a regression model to boost accuracy.
Toleva Borislava
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The Modified Principal Component Analysis Feature Extraction Method for the Task of Diagnosing Chronic Lymphocytic Leukemia Type B-CLL [PDF]
Journal of Universal Computer Science, 2020 The vast majority of medical problems are characterised by the relatively high spatial dimensionality of the task, which becomes problematic for many classic pattern recognition algorithms due to the well-known phenomenon of the curse of dimensionality ...
Mariusz Topolski
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Dynamic Functional Principal Components [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014 SummaryWe address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such functional time series frequently arise, for example, when a continuous time process is segmented into some smaller natural units, such as days. Then each X t represents one intraday curve.
Hörmann, Siegfried +2 more
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Principal Components Analysis Utility in the Livestock Field
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 2016 Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets.
Ancuta Simona Rotaru +3 more
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Revista Ambiente & Água, 2021 “Erodibility” is a characteristic of the soil that represents the susceptibility with which its particles from the most superficial layer are taken and transported to lower places by erosive agents, causing environmental and economic damages.
Lucivânia Izidoro da Silva +6 more
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Procedure for the Selection of Principal Components in Principal Components Regression [PDF]
Korean Journal of Applied Statistics, 2010 Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition
Bu-Yong Kim, Myung-Hee Shin
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