EstBiCop {CopulaInference} | R Documentation |

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.

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

`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). |

`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 |

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.

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

[Package *CopulaInference* version 0.5.0 Index]