Algebraic Techniques for Gaussian Models

Simple item page
dc.creatorDrton, Mathias
dc.date2006-10-23
dc.date.accessioned2026-07-07T08:08:16Z
dc.date.available2026-07-07T08:08:16Z
dc.descriptionMany statistical models are algebraic in that they are defined by polynomial constraints or by parameterizations that are polynomial or rational maps. This opens the door for tools from computational algebraic geometry. These tools can be employed to solve equation systems arising in maximum likelihood estimation and parameter identification, but they also permit to study model singularities at which standard asymptotic approximations to the distribution of estimators and test statistics may no longer be valid. This paper demonstrates such applications of algebraic geometry in selected examples of Gaussian models, thereby complementing the existing literature on models for discrete variables.
dc.description10 pages
dc.identifierhttps://arxiv.org/abs/math/0610679
dc.identifierhttp://arxiv.org/abs/math/0610679
dc.identifierPrague Stochastics 2006 (Eds. Mari Huskova, Martin Janzura), pp. 81-90, Matfyzpress, Charles University Prague
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131206
dc.subjectStatistics Theory
dc.subject62H05; 62H15
dc.titleAlgebraic Techniques for Gaussian Models
dc.typetext

Files

Collections

arXiv