vines (version 1.1.5)

vineFit: Vine Inference

Description

Estimate a vine model from multivariate data in the unit hypercube. Data can be pseudo-observations constructed from empirical or parametric marginal cumulative distribution functions.

Usage

vineFit(type, data, method = "ml", ...)

Arguments

type

Type of vine. Supported values: "CVine" and "DVine".

data

Data matrix of pseudo-observations.

method

Inference method. Supported values: "ml" (Maximum Likelihood).

...

Additional arguments for the inference method.

Value

A vineFit object or a subclass with specific information about inference method used. The vine slot of this object contains the fitted Vine object.

Details

The "ml" (Maximum Likelihood) method starts with the sequential estimation procedure described in (Aas et al., 2009) and then executes a numerical maximization of the full log-likelihood of the vine. The sequential procedure is used to determine the family and the initial values of the parameters of each bivariate copula in the decomposition. Additional arguments for this method are:

References

Aas, K. and Czado, C. and Frigessi, A. and Bakken, H. (2009) Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44, 182--198.

Brechmann, E. C. (2010) Truncated and simplified regular vines and their applications. Diploma thesis. Technische Universitaet Muenchen.

Brechmann, E. C. and Czado, C. and Aas, K. (2010) Truncated regular vines in high dimensions with application to financial data. Norwegian Computing Center, NR. Note SAMBA/60/10.

See Also

CVine, DVine, vineFit, vineFitML.

Examples

data <- matrix(runif(5 * 100), ncol = 5, nrow = 100)
colnames(data) <- c("A", "B", "C", "D", "E")

selectCopula <- function (vine, j, i, x, y) {
    data <- cbind(x, y)
    fit <- fitCopula(normalCopula(), data, method = "itau")
    fit@copula
}
fit <- vineFit("DVine", data, method = "ml", 
               selectCopula = selectCopula, 
               optimMethod = "")

show(fit)
show(fit@vine)