Publication

Extremes: spatial parametric modeling

Anthony Christopher Davison
2012
Book Chapters

Abstract

Statistics of spatial extremes is developing very rapidly, owing to the demands of applications in the environmental sciences and the insurance and risk industries. This entry sketches the main ideas, based on classical extreme-value statistics. The two main threads of work are the fitting of max-stable models to spatial extremes, and the use of latent variable models, the latter typically in a Bayesian context.

Official source

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Anthony Christopher Davison
2012
Book Chapters

Abstract

Official source

Related concepts (23)

Latent variable model

A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent variables. It is assumed that the responses on the indicators or manifest variables are the result of an individual's position on the latent variable(s), and that the manifest variables have nothing in common after controlling for the latent variable (local independence).

Latent and observable variables

In statistics, latent variables (from Latin: present participle of lateo, “lie hidden”) are variables that can only be inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured. Such latent variable models are used in many disciplines, including political science, demography, engineering, medicine, ecology, physics, machine learning/artificial intelligence, bioinformatics, chemometrics, natural language processing, management, psychology and the social sciences.

Bayesian network

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). It is one of several forms of causal notation. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.

Related publications (4)

Extremes: spatial parametric modeling

Chat with Graph Search

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Enhanced measurement equations for latent class choice models

Seismic assessment of existing buildings, Approach based on the risk used in Switzerland