EstBiCop: Parameter estimation for bivariate copula-based models with...

In CopulaInference: Estimation and Goodness-of-Fit of Copula-Based Models with Arbitrary Distributions

Parameter estimation for bivariate copula-based models with arbitrary distributions

Description

Computes the estimation of the parameters of a copula-based model with arbitrary distributions, i.e, possibly mixtures of discrete and continuous distributions. Parametric margins are allowed. The estimation is based on a pseudo-likelihood adapted to ties.

Usage

EstBiCop(
  data = NULL,
  family,
  rotation = 0,
  Fx = NULL,
  Fxm = NULL,
  Fy = NULL,
  Fym = NULL
)

Arguments

data	Matrix or data frame with 2 columns (X,Y). Can be pseudo-observations. If NULL, Fx and Fy must be provided.
family	Copula family: "gaussian", "t", "clayton", "frank", "gumbel", "joe", "plackett”, "bb1", "bb6", "bb7","bb8","ncs-gaussian", "ncs-clayton", "ncs-gumbel", "ncs-frank", "ncs-joe","ncs-plackett".
rotation	Rotation: 0 (default value), 90, 180, or 270.
Fx	Marginal cdf function applied to X (default is NULL).
Fxm	Left-limit of marginal cdf function applied to X default is NULL).
Fy	Marginal cdf function applied to Y (default is NULL).
Fym	Left-limit of marginal cdf function applied to Y (default is NULL).

Value

par	Copula parameters
family	Copula family
rotation	Rotation value
tauth	Kendall's tau corresponding to the estimated parameter
tauemp	Empirical Kendall's tau (from the multilinear empirical copula)
rhoSth	Spearman's rho corresponding to the estimated parameter
rhoSemp	Empirical Spearman's tau (from the multilinear empirical copula)
loglik	Log-likelihood
aic	Aic value
bic	Bic value
data	Matrix of values (could be (Fx,Fy))
F1	Cdf of X (Fx if provided, empirical otherwise)
F1m	Left-limit of F1 (Fxm if provided, empirical otherwise)
F2	Cdf of Y (Fy if provided, empirical otherwise)
F2m	Left-limit of F2 (Fym if provided, empirical otherwise)
ccdfx	Conditional cdf of X given Y and it left limit
ccdfxm	Left-limit of ccdfx
ccdfy	Conditional cdf of Y given X and it left limit
ccdfym	Left-limit of ccdfy

References

Nasri & Remillard (2023). Identifiability and inference for copula-based semiparametric models for random vectors with arbitrary marginal distributions. arXiv 2301.13408.

Nasri (2020). On non-central squared copulas. Statistics and Probability Letters.

Examples

set.seed(2)
data = matrix(rpois(20,1),ncol=2)
out0=EstBiCop(data,"gumbel")

CopulaInference documentation built on April 21, 2023, 9:07 a.m.