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Likelihood‐based inference for correlated diffusions
Author(s) -
Kalogeropoulos Konstantinos,
Dellaportas Petros,
Roberts Gareth O.
Publication year - 2011
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.10096
Subject(s) - mathematics , cholesky decomposition , quasi maximum likelihood , statistical inference , maximum likelihood , statistics , likelihood function , econometrics , physics , eigenvalues and eigenvectors , quantum mechanics
The authors address the problem of likelihood‐based inference for correlated diffusions. Such a task presents two issues; the positive definite constraints of the diffusion matrix and the likelihood intractability. The first issue is handled by using the Cholesky factorization on the diffusion matrix. To deal with the likelihood unavailability, a generalization of the data augmentation framework of Roberts and Stramer [Roberts and Stramer (2001) Biometrika 88(3), 603–621] to d ‐dimensional correlated diffusions, including multivariate stochastic volatility models, is given. The methodology is illustrated through simulated and real data sets. The Canadian Journal of Statistics 39: 52–72; 2011 © 2011 Statistical Society of Canada