New PDF release: Computer Intensive Methods in Statistics

By D. A. Stephens, A. F. M. Smith (auth.), Prof. Dr. Wolfgang Härdle, Prof. Léopold Simar (eds.)

ISBN-10: 3642524680

ISBN-13: 9783642524684

ISBN-10: 3790806773

ISBN-13: 9783790806779

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1, the matrix of V is as construc ted. An elegant and efficient way of construc tion follows (vuv '] t ! 1+Vz1V~~V1z When tion can 1 Vh. 1 is construc ted be applied recursively. inverted -Wishar t matrices can an upper as triangul ar An algorithm be construc ted for generati ng this using the matrix, decompo si- square roots of decompo sition, see generate the Zellner, Bauwens and Van Dijk (1988). It square is course of roots of the also possible inverted -Wishar t as suggeste d random earlier, matrices as to inverses of square 40 of roots distributed random be calculated efficiently.

BAUWENS CORE, Universite Catholique de Louvain, 34 Voie du Roman Pays, 1348 Louvain-La-Neuve, BELGIUM A. RASOUERO GREQE, Ecole des Hautes Etudes en Sciences Sociales, 2 Rue de la Charite, 13002 Marseille, FRANCE Key Words: Residual Autocorrelation, Regression Model, Bayesian Inference. HPD Region, Power, Augmented Regressions, Abstract We evaluate two tests of residual autocorrelation in the linear regression model in a Bayesian framework. Each test checks if an approximate highest posterior density region of the parameters of the autoregressive process of the error contains the null hypothesis.

I) Since o is a non-linear function of e, the posterior density of o is not known analytically. 4) and calculate for each drawing the corresponding value of o. By simple averaging of these N values and of functions of them we can obtain (if N is large enough) a very good approximation of the posterior expectation E(o I y) and of the variance-covaria nce matrix V(o I y). The covariance matrix Cov(o,o: I y) can be computed likewise. E(o: I y) and V(o: I y) being known analytically, this step provides us with E(

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Computer Intensive Methods in Statistics by D. A. Stephens, A. F. M. Smith (auth.), Prof. Dr. Wolfgang Härdle, Prof. Léopold Simar (eds.)

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