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In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as X ˆ = k K=1 dkukvk T , where dk, uk, and vk m...
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets of...
Let x1 denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian varates. Equivalently, x1 is the largest principal component vari...
A basic assumption concerned with general linear regression model is that there is no correlation (or no multicollinearity) between the explanatory variables. When this assumption is not satisfied, th...
Factors conditioning formation and properties of suspended matter resting on the sea floor (Fluffy Layer Suspended Matter - FLSM) in the Odra river mouth - Arkona Deep system (southern Baltic Sea) wer...
A correct description of fine (diameter <1 μm) and ultrafine (<0.1 μm) aerosol particles in urban areas is of interest for particle exposure assessment but also basic atmospheric research. We examined...
In this paper the bootstrap theories, which are based on the author's former paper, of M-typ eprincipal components and dispersion matrices and M-type PP tests for multivariate locationand scale are ob...

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