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Publication

A characterization of the normal distribution using stationary max-stable processes

Sebastian Engelke
2016
Journal Articles

Abstract

Consider a max-stable process of the form , , where are points of the Poisson process with intensity u (-2)du on (0,a), X (i) , , are independent copies of a random d-variate vector X (that are independent of the Poisson process), and is a function. We show that the process eta is stationary if and only if X has multivariate normal distribution and kappa(t)-kappa(0) is the cumulant generating function of X. In this case, eta is a max-stable process introduced by R. L. Smith.

Official source

Sebastian Engelke
2016
Journal Articles

Abstract

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (23)

Multivariate normal distribution

In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem.

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Log-normal distribution

In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y, X = exp(Y), has a log-normal distribution. A random variable which is log-normally distributed takes only positive real values.

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