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Nonlinear principal components, II: Characterization of normal distributions
Nonlinear principal components are defined for normal random vectors. Their properties are investigated and interpreted in terms of the classical linear principal component analysis. A characterization theorem is proven. All these results are employed to
Salinelli, Ernesto
core
Collocation of Next-Generation Operators for Computing the Basic Reproduction Number of Structured Populations. [PDF]
J Sci Comput, 2020 Breda D +3 more
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Approximation of eigenvalues for unbounded Jacobi matrices using finite submatrices
Open Mathematics, 2014 Monvel Anne, Zielinski Lech
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Perron-Frobenius and Krein-Rutman theorems for tangentially positive operators
Open Mathematics, 2012 Kanigowski Adam, Kryszewski Wojciech
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Negative Eigenvalue Estimates for the 1D Schrödinger Operator with Measure-Potential. [PDF]
Ann Henri PoincareFulsche R, Nursultanov M, Rozenblum G.
europepmc +1 more source
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Crystallographic Groups, Strictly Tessellating Polytopes, and Analytic Eigenfunctions
American Mathematical Monthly, 2021Julie Rowlett
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Quantitative Inequalities for the Expected Lifetime of Brownian Motion
Michigan Mathematical Journal, 2021exaly
Integral Operators and Delay Differential Equations
Journal of Integral Equations and Applications, 2006Florian A. Potra, David E Gilsinn
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Left Definite Sturm-Liouville Problems With Eigenparameter Dependent Boundary Conditions
Differential and Integral Equations, 1999P A Binding, Patrick J Browne
exaly