A bivariate F distribution with marginals on arbitrary numerator and denominator degrees of freedom, and related bivariate beta and t distributions

The classical bivariate F distribution arises from ratios of chi-squared random variables with common denominators. A consequent disadvantage is that its univariate F marginal distributions have one degree of freedom parameter in common. In this paper, we add a further independent chi-squared random variable to the denominator of one of the ratios and explore the extended bivariate F distribution, with marginals on arbitrary degrees of freedom, that results. Transformations linking F, beta and skew t distributions are then applied componentwise to produce bivariate beta and skew t distributions which also afford marginal (beta and skew t) distributions with arbitrary parameter values. We explore a variety of properties of these distributions and give an example of a potential application of the bivariate beta distribution in Bayesian analysis.

[1]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[2]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[3]  M. C. Jones,et al.  A skew extension of the t‐distribution, with applications , 2003 .

[4]  A. W. Kemp,et al.  Continuous Bivariate Distributions, Emphasising Applications , 1991 .

[5]  T. Pham-Gia,et al.  The generalized beta- and F-distributions in statistical modelling , 1989 .

[6]  W. R. Buckland,et al.  Distributions in Statistics: Continuous Multivariate Distributions , 1973 .

[7]  R. Nelsen An Introduction to Copulas , 1998 .

[8]  Ingram Olkin,et al.  A bivariate beta distribution , 2003 .

[9]  I. M. Pyshik,et al.  Table of integrals, series, and products , 1965 .

[10]  Alan M. Zaslavsky,et al.  An Empirical Bayes Model for Markov-Dependent Binary Sequences with Randomly Missing Observations , 1995 .

[11]  Mei-Ling Ting Lee,et al.  Properties and applications of the sarmanov family of bivariate distributions , 1996 .

[12]  H. Joe Multivariate models and dependence concepts , 1998 .

[13]  K. Chaloner,et al.  Assessment of a Beta Prior Distribution: PM Elicitation , 1983 .

[14]  A. O'Hagan,et al.  Statistical Methods for Eliciting Probability Distributions , 2005 .

[15]  Melvin R. Novick,et al.  Multivariate Generalized Beta Distributions with Applications to Utility Assessment , 1982 .

[16]  M. C. Jones Multivariate t and beta distributions associated with the multivariate F distribution , 2002 .

[17]  Chin-Diew Lai,et al.  Continuous Bivariate Distributions, Emphasising Applications , 1992 .

[18]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[19]  R. L. Winkler The Assessment of Prior Distributions in Bayesian Analysis , 1967 .

[20]  N. Balakrishnan,et al.  Continuous Bivariate Distributions , 2009 .

[21]  C. Chatfield Continuous Univariate Distributions, Vol. 1 , 1995 .

[22]  M. C. Jones A dependent bivariate t distribution with marginals on different degrees of freedom , 2002 .

[23]  Jian-Ming Jin,et al.  Computation of special functions , 1996 .

[24]  A. McNeil Multivariate t Distributions and Their Applications , 2006 .

[25]  A. Kimball On Dependent Tests of Significance in the Analysis of Variance , 1951 .