Likelihood ratio tests of separable or double separable covariance structure, and the empirical null distribution
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The focus in this thesis is on the calculations of an empirical null distributionfor likelihood ratio tests testing either separable or double separable covariancematrix structures versus an unstructured covariance matrix. These calculationshave been performed for various dimensions and sample sizes, and are comparedwith the asymptotic χ2-distribution that is commonly used as an approximative distribution. Tests of separable structures are of particular interest in cases when data iscollected such that more than one relation between the components of the observationis suspected. For instance, if there are both a spatial and a temporalaspect, a hypothesis of two covariance matrices, one for each aspect, is reasonable.
Place, publisher, year, edition, pages
2011. , 57 p.
Empirical Null Distribution, Flip-flop Algorithm, Kronecker Product, Likelihood Ratio Test, Matrix Normal Distribution, Maximum Likelihood Estimator, Multilinear Normal Distribution, Separable Covariance Structure, Statistics.
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-69738ISRN: LiTH-MAT-EX--11/09--SEOAI: oai:DiVA.org:liu-69738DiVA: diva2:432352
Subject / course
UppsokPhysics, Chemistry, Mathematics