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A multivariate von mises distribution with applications to bioinformatics
Author(s) -
Mardia Kanti V.,
Hughes Gareth,
Taylor Charles C.,
Singh Harshinder
Publication year - 2008
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550360110
Subject(s) - bivariate analysis , univariate , von mises distribution , multivariate statistics , mathematics , joint probability distribution , extension (predicate logic) , statistics , marginal distribution , multivariate normal distribution , distribution (mathematics) , maximum likelihood , von mises yield criterion , conditional probability distribution , econometrics , computer science , random variable , mathematical analysis , engineering , finite element method , structural engineering , programming language
Motivated by problems of modelling torsional angles in molecules, Singh, Hnizdo & Demchuk (2002) proposed a bivariate circular model which is a natural torus analogue of the bivariate normal distribution and a natural extension of the univariate von Mises distribution to the bivariate case. The authors present here a multivariate extension of the bivariate model of Singh, Hnizdo & Demchuk (2002). They study the conditional distributions and investigate the shapes of marginal distributions for a special case. The methods of moments and pseudo‐likelihood are considered for the estimation of parameters of the new distribution. The authors investigate the efficiency of the pseudo‐likelihood approach in three dimensions. They illustrate their methods with protein data of conformational angles