Signal processing

Copulas in signal processing

A copula is a mathematical function that provides a relationship between marginal distributions of random variables and their joint distributions. Copulas are important because it represents a dependence structure without using marginal distributions. Copulas have been widely used in the field of finance, but their use in signal processing is relatively new. Copulas have been employed in the field of wireless communication for classifying radar signals, change detection in remote sensing applications, and EEG signal processing in medicine. In this article, a short introduction to copulas is presented, followed by a mathematical derivation to obtain copula density functions, and then a section with a list of copula density functions with applications in signal processing. (Wikipedia).

Video thumbnail

Stat Pills 1: Copulas

In this video, extracted from one of my courses, I briefly speak about copulas, as tools to model multivariate random variables and distributions.

From playlist Statistical Pills

Video thumbnail

Introduction to Signal Processing

http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introductory overview of the field of signal processing: signals, signal processing and applications, phi

From playlist Introduction and Background

Video thumbnail

Determining Signal Similarities

Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox™. For more on Signal Processing To

From playlist Signal Processing and Communications

Video thumbnail

Signal Processing Framework

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introduces three pervasive problems in signal processing: filtering, equalization, and system identification.

From playlist Introduction and Background

Video thumbnail

Notation and Basic Signal Properties

http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Signals as functions, discrete- and continuous-time signals, sampling, images, periodic signals, displayi

From playlist Introduction and Background

Video thumbnail

Bruno Rémillard: Copulas based inference for discrete or mixed data

Abstract : In this talk I will introduce the multilinear empirical copula for discrete or mixed data and its asymptotic behavior will be studied. This result will then be used to construct inference procedures for multivariate data. Applications for testing independence will be presented.

From playlist Probability and Statistics

Video thumbnail

Introduction to the z-Transform

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introduces the definition of the z-transform, the complex plane, and the relationship between the z-transform and the discrete-time Fourier transfor

From playlist The z-Transform

Video thumbnail

MOR102 - Morphological Operations

Morphological operations define how words can be modified, i.e. what type of operation is applied to change a word. This can be done by means of adding items or concatenating two or more entities or it can be achieved by non-concatenative operations that somehow modify the base. This unit

From playlist VLC101 - Linguistic Fundamentals

Video thumbnail

Performing Peak Analysis

Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Determine the period of a signal by measuring the distance between the peaks, and find peaks in a noisy signal using Signal Processing Toolbox™. For more on Signal Process

From playlist Signal Processing and Communications

Video thumbnail

Pavel Krupskiy - Conditional Normal Extreme-Value Copulas.

Dr Pavel Krupskiy (University of Melbourne) presents “Conditional Normal Extreme-Value Copulas”, 14 August 2020. Seminar organised by UNSW Sydney.

From playlist Statistics Across Campuses

Video thumbnail

Brief History of Signal Processing

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Describes several key events in development of the field of signal processing.

From playlist Introduction and Background

Video thumbnail

Philippe Naveau: Detecting seasonality changes in multivariate extremes from climatological time ...

Many effects of climate change seem to be reflected not in the mean temperatures, precipitation or other environmental variables, but rather in the frequency and severity of the extreme events in the distributional tails. The most serious climate-related disasters are caused by compound ev

From playlist Probability and Statistics

Video thumbnail

Pricing Credit Derivatives by Srikanth Iyer

Modern Finance and Macroeconomics: A Multidisciplinary Approach URL: http://www.icts.res.in/program/memf2015 DESCRIPTION: The financial meltdown of 2008 in the US stock markets and the subsequent protracted recession in the Western economies have accentuated the need to understand the dy

From playlist Modern Finance and Macroeconomics: A Multidisciplinary Approach

Video thumbnail

The Distortions of Finance

Seminar about distortion functions and Lorenz curves in finance.

From playlist Talks and Interviews

Video thumbnail

IMS Public Lecture: Mathematics and the Financial Crisis

Paul Embrechts, Swiss Federal Institute of Technology (ETH), Zurich

From playlist Public Lectures

Video thumbnail

Characterization of Random, Multivariate Signals

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Multivariable (vector) probability density function representations, including the multivariate Gaussian density. The covariance matrix and in

From playlist Random Signal Characterization

Video thumbnail

SYN121 - The Verb in PDE - Part II

In this second of a series of three E-Lectures Prof. Handke discusses the distinction between lexical and auxiliary verbs using the NICE-criteria as well as additional morpho-syntactic criteria. This includes a distinction between primary and secondary auxiliary verbs.

From playlist VLC201 - The Structure of English

Related pages

Signal processing | Marginal distribution | Chain rule | Probability density function | Cumulative distribution function | Copula (probability theory)