morgenstern: Morgenstern's Bivariate Distribution Family Function

Description

Estimate the association parameter of Morgenstern's bivariate distribution using maximum likelihood estimation.

Usage

morgenstern(lapar="rhobit", earg=list(), iapar=NULL, tola0=0.01,
            method.init=1)

Arguments

lapar

Link function applied to the association parameter $\alpha$, which lies between $-1$ and $1$. See Links for more choices.

earg

List. Extra argument for the link. See earg in Links for general information.

iapar

Numeric. Optional initial value for $\alpha$. By default, an initial value is chosen internally. If a convergence failure occurs try assigning a different value. Assigning a value will override the argument method.init.

tola0

Positive numeric. If the estimate of $\alpha$ has an absolute value less than this then it is replaced by this value. This is an attempt to fix a numerical problem when the estimate is too close to zero.

method.init

An integer with value 1 or 2 which specifies the initialization method. If failure to converge occurs try the other value, or else specify a value for ia.

Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm and vgam.

Details

The cumulative distribution function is

P (Y_{1} \leq y_{1}, Y_{2} \leq y_{2}) = e^{- y_{1} - y_{2}} (1 + α [1 - e^{- y_{1}}] [1 - e^{- y_{2}}]) + 1 - e^{- y_{1}} - e^{- y_{2}}

for $\alpha$ between $-1$ and $1$. The support of the function is for $y_1>0$ and $y_2>0$. The marginal distributions are an exponential distribution with unit mean. When $\alpha = 0$ then the random variables are independent, and this causes some problems in the estimation process since the distribution no longer depends on the parameter.

A variant of Newton-Raphson is used, which only seems to work for an intercept model. It is a very good idea to set trace=TRUE.

References

Castillo, E., Hadi, A. S., Balakrishnan, N. Sarabia, J. S. (2005) Extreme Value and Related Models with Applications in Engineering and Science, Hoboken, N.J.: Wiley-Interscience.

Examples

Run this code

n = 1000
ymat = cbind(rexp(n), rexp(n))
plot(ymat)
fit = vglm(ymat ~ 1, fam=morgenstern, trace=TRUE)
fit = vglm(ymat ~ 1, fam=morgenstern, trace=TRUE, crit="coef")
coef(fit, matrix=TRUE)
Coef(fit)
fitted(fit)[1:5,]

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