Results 271 to 280 of about 689,927 (311)

PRINCIPAL COMPONENT VALUE AT RISK

International Journal of Theoretical and Applied Finance, 2000
Value at risk (VaR) is an industrial standard for monitoring financial risk in an investment portfolio. It measures potential losses within a given confidence interval. The implementation, calculation, and interpretation of VaR contains a wealth of mathematical issues that are not fully understood.
Brummelhuis, R., Córdoba, A., Quintanilla, M., Seco, L.   +3 more
openaire   +2 more sources

Nonlinear principal component analysis to preserve the order of principal components

Neurocomputing, 2004
Principal component analysis (PCA) is an effective method of linear dimensional reduction. Because of its simplicity in theory and implementation, it is often used for analyses in various disciplines. However, because of its linearity, PCA is not always suitable, and has redundancy in expressing data.
Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto   +2 more
openaire   +1 more source

Principal Components and Principal Clusters

Journal of Information and Optimization Sciences, 1987
Abstract The use of principal components to reduce the number of dimensions so that graphieal representation of the data is possible has been developed. One. of the most important applications is the connexion with cluster analysis. It has not been defined the criteria by which to decide whether there is any justification for dividing a set of ...
Haruo Miyazaki, Youichi Seki
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Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire   +5 more sources

Directed Principal Component Analysis

Operations Research, 2014
We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in ...
Yi-Hao Kao, Benjamin Van Roy
openaire   +2 more sources

The efficient cross-validation of principal components applied to principal component regression

Statistics and Computing, 1995
The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components.
Bart J. A. Mertens, Tom Fearn, Michael Thompson   +2 more
openaire   +1 more source

Which principal components to utilize for principal component regression

Journal of Chemometrics, 1992
AbstractPrincipal components (PCs) for principal component regression (PCR) have historically been selected from the top down for a reliable predictive model. That is, the PCs are arranged in a list starting with the most informative (PC associated with the largest singular value) and proceeding to the least informative (PC associated with the smallest
Jon M. Sutter, John H. Kalivas, Patrick M. Lang   +2 more
openaire   +1 more source

Kernel Principal Component Analysis

1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller   +2 more
openaire   +3 more sources

Robust Principal Components Regression

2002
We consider the multivariate linear regression model with p explanatory variables X and q ≥ 1 response variables Y. Moreover we assume that the regressors are multicollinear. This situation often occurs in the calibration of chemometrical data, where the X-variables correspond with spectra that are measured at many frequencies.
Verboven, S., Hubert, M.
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Segmented principal component transform–principal component analysis

Chemometrics and Intelligent Laboratory Systems, 2005
Abstract A new approach to perform Principal Component Analysis (PCA) on very wide matrices is proposed in this work. The procedure is based on an extension of the Principal Component Transform (PCT) concept—the PCT being applied to non-superimposed segments of the data matrix.
António S. Barros, Douglas N. Rutledge
openaire   +1 more source