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Books on the topic 'Hilbert serie'

Author: Grafiati
Published: 9 March 2023

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1

1937-, Huang N. E., and Shen Samuel S, eds. The Hilbert-Huang transform and its applications. New Jersey: World Scientific, 2005.

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2

En-Ching, Hsu, ed. Hilbert-Huang transform analysis of hydrological and environmental time series. Dordrecht: Springer, 2008.

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3

Rao, A. Ramachandra. Hilbert-Huang transform analysis of hydrological and environmental time series. Dordrecht: Springer, 2008.

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4

Rao, A. Ramachandra. Hilbert-Huang transform analysis of hydrological and environmental time series. Dordrecht: Springer, 2008.

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5

Alberto, Corso, and Polini Claudia 1966-, eds. Commutative algebra and its connections to geometry: Pan-American Advanced Studies Institute, August 3--14, 2009, Universidade Federal de Pernambuco, Olinda, Brazil. Providence, R.I: American Mathematical Society, 2011.

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6

Shen, Samuel S., and N. E. Huang. Hilbert-Huang Transform and Its Applications. World Scientific Publishing Co Pte Ltd, 2014.

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7

United States Geological Survey. Hilbert quadrangle, Wisconsin, 1992: 7.5 minute series (topographic). Wisconsin Geological and Natural History Survey, 1996.

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8

Nakajima, Hiraku. Lectures on Hilbert Schemes of Points on Surfaces (University Lecture Series). American Mathematical Society, 1999.

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9

Strongly Irreducible Operators on Hilbert Space (Research Notes in Mathematics Series). Chapman & Hall/CRC, 1998.

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10

Hilbert-Huang Transform Analysis Of Hydrological And Environmental Time Series. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6454-8.

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11

Rao, A. R., and E. C. Hsu. Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series. Springer Netherlands, 2010.

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12

Koiter, W. T., Gilbert Helmberg, and H. A. Lauwerier. Introduction to Spectral Theory in Hilbert Space: North-Holland Series in Applied Mathematics and Mechanics. Elsevier Science & Technology Books, 2014.

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13

Vicente, José Luis, Matías Rafti, and Alberto Gustavo Albesa. Matemáticas especiales para fisicoquímicos. Editorial de la Universidad Nacional de La Plata, 2018. http://dx.doi.org/10.35537/10915/70953.

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La obra trata sobre las bases teóricas y algunas aplicaciones fisicoquímicas de lo que constituyen los fundamentos del análisis en el campo complejo y las ecuaciones diferenciales. La primera parte comprende el estudio de funciones en una variable compleja. Se desarrollan los temas de derivación, integración y series de potencias, en ese orden, con especial énfasis en los aspectos geométricos de tales desarrollos. La segunda parte cubre los temas de ecuaciones diferenciales, en especial el caso lineal. Se subdivide en ecuaciones diferenciales ordinarias y en derivadas parciales. Para las ecuaciones diferenciales ordinarias se discuten los problemas de valores iniciales y de contorno. También se presentan breves nociones de espacios de Hilbert y de distribuciones.
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14

Ordinary Differential Equations and Hilbert's 16th Problem (Series in Pure Mathematics). World Scientific Pub Co Inc, 1992.

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15

Orthonormal Series Estimators. Hackensack, New Jersey, USA: WSPC, 2019.

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16

(Translator), Ognjen Milatovic, and Vladimir A. Marchenko (Foreword), eds. Spectral Analysis of Differential Operators: Interplay Between Spectral and Oscillatory Properties (World Scientific Monograph Series in Mathematics, Vol. 7). World Scientific Publishing Company, 2005.

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17

Geer, Gerard van der. Hilbert Modular Surfaces (Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics). Springer, 1987.

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18

Mas, André, and Besnik Pumo. Linear Processes for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.3.

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This article provides an overview of the basic theory and applications of linear processes for functional data, with particular emphasis on results published from 2000 to 2008. It first considers centered processes with values in a Hilbert space of functions before proposing some statistical models that mimic or adapt the scalar or finite-dimensional approaches for time series. It then discusses general linear processes, focusing on the invertibility and convergence of the estimated moments and a general method for proving asymptotic results for linear processes. It also describes autoregressive processes as well as two issues related to the general estimation problem, namely: identifiability and the inverse problem. Finally, it examines convergence results for the autocorrelation operator and the predictor, extensions for the autoregressive Hilbertian (ARH) model, and some numerical aspects of prediction when the data are curves observed at discrete points.
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19

An Introduction to Integral Transforms and Their Applications. Burlington, Canada: Arcler Press, 2018.

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