1.

Record Nr.

UNISA990005520100203316

Titolo

Corso di scienze delle finanze / a cura di Paolo Bosi

Pubbl/distr/stampa

Bologna : Il mulino, copyr. 2003

Edizione

[3.ed]

Descrizione fisica

443 p. : graf. ; 24 cm

Collana

Strumenti. Economia

Disciplina

336

Soggetti

Finanza pubblica - manuali

Collocazione

300 336 BOS

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9910458158503321

Autore

Zoubir Abdelhak M.

Titolo

Bootstrap techniques for signal processing / / Abdelhak M. Zoubir, D. Robert Iskander [[electronic resource]]

Pubbl/distr/stampa

Cambridge : , : Cambridge University Press, , 2004

ISBN

1-107-14842-1

1-280-47787-3

9786610477876

0-511-19529-X

0-511-19595-8

0-511-19389-0

0-511-33144-4

0-511-53671-2

0-511-19463-3

Descrizione fisica

1 online resource (xiv, 217 pages) : digital, PDF file(s)

Disciplina

621.382/2

Soggetti

Signal processing - Mathematics

Image processing - Mathematics

Bootstrap (Statistics)



Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Nota di bibliografia

Includes bibliographical references (p. 201-214) and index.

Nota di contenuto

Cover; Half-title; Title; Copyright; Contents; Preface; Notations; 1 Introduction; 2 The bootstrap principle; 3 Signal detection with the bootstrap; 4 Bootstrap model selection; 5 Real data bootstrap applications; Appendix 1 Matlab codes for the examples; Appendix 2 Bootstrap Matlab Toolbox; References; Index

Sommario/riassunto

The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering and automotive engineering.