Covariance chains
暂无分享,去创建一个
[1] N. Wermuth,et al. Miscellanea. On the generation of the chordless four-cycle , 2000 .
[2] J. Sargan. THE ESTIMATION OF ECONOMIC RELATIONSHIPS USING INSTRUMENTAL VARIABLES , 1958 .
[3] T. W. Anderson,et al. Maximum-likelihood estimation of the parameters of a multivariate normal distribution☆ , 1985 .
[4] S. J. Press,et al. Applied multivariate analysis : using Bayesian and frequentist methods of inference , 1984 .
[5] A. Dempster. Elements of Continuous Multivariate Analysis , 1969 .
[6] T. Rao,et al. Tensor Methods in Statistics , 1989 .
[7] N. Wermuth,et al. Joint response graphs and separation induced by triangular systems , 2004 .
[8] Anja Vogler,et al. An Introduction to Multivariate Statistical Analysis , 2004 .
[9] N. Wermuth. Linear Recursive Equations, Covariance Selection, and Path Analysis , 1980 .
[10] H. B. Heywood,et al. On finite sequences of real numbers , 1931 .
[11] E. Christensen. Statistical Properties of I-projections Within Exponential Families , 1989 .
[12] N. Wermuth,et al. Linear Dependencies Represented by Chain Graphs , 1993 .
[13] R. Fisher,et al. On the Mathematical Foundations of Theoretical Statistics , 1922 .
[14] Giovanni M. Marchetti,et al. Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm , 2006 .
[15] T. Speed,et al. Gaussian Markov Distributions over Finite Graphs , 1986 .
[16] Thomas S. Richardson,et al. A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence , 2002, UAI.
[17] N. Wermuth,et al. On the identification of path analysis models with one hidden variable , 2005 .
[18] Nanny Wermuth,et al. An approximation to maximum likelihood estimates in reduced models , 1990 .
[19] Roderick P. McDonald,et al. What can we learn from the path equations?: Identifiability, constraints, equivalence , 2002 .
[20] S. S. Wilks. CERTAIN GENERALIZATIONS IN THE ANALYSIS OF VARIANCE , 1932 .
[21] H. Kiiveri. An incomplete data approach to the analysis of covariance structures , 1987 .
[22] S. Orbom,et al. When Can Association Graphs Admit A Causal Interpretation? , 1993 .
[23] Joe Whittaker,et al. The Isserlis matrix and its application to non-decomposable graphical Gaussian models , 1998 .
[24] M. Frydenberg. The chain graph Markov property , 1990 .
[25] Nanny Wermuth,et al. On association models defined over independence graphs , 1998 .
[26] J. Gram. Ueber die Entwickelung reeller Functionen in Reihen mittelst der Methode der kleinsten Quadrate. , 1883 .
[27] W. G. Cochran. The Omission or Addition of an Independent Variate in Multiple Linear Regression , 1938 .
[28] Peter Cheeseman,et al. Selecting Models from Data: Artificial Intelligence and Statistics IV , 1994 .
[29] J. Pearl,et al. A New Identification Condition for Recursive Models With Correlated Errors , 2002 .
[30] T. Richardson,et al. Multimodality of the likelihood in the bivariate seemingly unrelated regressions model , 2004 .
[31] L. Isserlis. III. Formulae for determining the Mean Valuei of Products of Deviations of mixed Moment Coefficients in two to eight Variables In Samples taken from a limited Population. , 1918 .
[32] J. Aitchison,et al. Maximum-Likelihood Estimation of Parameters Subject to Restraints , 1958 .
[33] T. W. Anderson. Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure , 1973 .
[34] G. Kauermann. On a dualization of graphical Gaussian models , 1996 .