marginal.gctsc: Marginal Models for Copula Time Series

In gctsc: Gaussian and Student-t Copula Models for Count Time Series

Marginal Models for Copula Time Series

Description

The following marginal models are currently available:

poisson.marg(link = "log")

Poisson distribution.

negbin.marg(link = "log")

Negative binomial distribution.

binom.marg(link = "logit", size)

Binomial distribution with fixed number of trials.

bbinom.marg(link = "logit", size)

Beta-binomial with overdispersion.

zip.marg(link = "log")

Zero-inflated Poisson model.

zib.marg(link = "logit", size)

Zero-inflated binomial.

zibb.marg(link = "logit", size)

Zero-inflated beta-binomial with separate covariates for zero inflation.

Usage

poisson.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)

binom.marg(link = "logit", size = NULL, lambda.lower = NULL, lambda.upper = NULL)

zib.marg(link = "logit", size = NULL, lambda.lower = NULL, lambda.upper = NULL)

negbin.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)

zip.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)

bbinom.marg(link = "logit", size, lambda.lower = NULL, lambda.upper = NULL)

zibb.marg(link = "logit", size,  lambda.lower = NULL, lambda.upper = NULL)

Arguments

link	The link function for the mean (e.g., "log", "logit", "identity").
lambda.lower	Optional lower bounds on parameters.
lambda.upper	Optional upper bounds on parameters.
size	Number of trials (for binomial-type models).

Details

These functions define the marginal distributions used in copula-based count time series models.

Each marginal constructor returns an object of class "marginal.gctsc" which defines:

• start: a function to compute starting values.

• npar: number of parameters.

• bounds: truncation bounds on the latent Gaussian.

These marginals are designed to work with gctsc() and its related methods.

Value

A list of class "marginal.gctsc" representing the marginal model.

References

Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software, 34(2): 1–24.

Ferrari, S.L.P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics, 31(7): 799–815.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics, 6: 1517–1549.

See Also

gctsc, predict.gctsc, arma.cormat

Examples

poisson.marg(link = "identity")
zibb.marg(link = "logit", size = 24)

gctsc documentation built on March 20, 2026, 9:11 a.m.