Algebraic Techniques for Gaussian Models

Details Algebraic Techniques for Gaussian Models Algebraic Techniques for Gaussian Models

2006-10-23

arxiv.org/abs/math/0610679v1

Prague Stochastics 2006 (Eds. Mari Huskova, Martin Janzura), pp. 81-90, Matfyzpress, Charles University Prague

Mathematics

Statistics Theory

10 pages

Scientific paper

Many 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.

Affiliated with

Also associated with

No associations

Rating

Algebraic Techniques for Gaussian Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Algebraic Techniques for Gaussian Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Algebraic Techniques for Gaussian Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-277270