Generalised bayesian sample copula of order m

02/22/2022
by   Luis E. Nieto-Barajas, et al.
0

In this work we propose a semiparametric bivariate copula whose density is defined by a picewise constant function on disjoint squares. We obtain the maximum likelihood estimators which reduce to the sample copula under specific conditions. We carry out a full Bayesian analysis of the model and propose a spatial dependent prior distribution for the model parameters. This prior allows the parameters to borrow strength across neighbouring regions to produce smooth posterior estimates. We implement a simulation study and illustrate the performance of our model with a real dataset.

READ FULL TEXT

page 20

page 21

research
07/14/2021

Temporally Local Maximum Likelihood with Application to SIS Model

The parametric estimators applied by rolling are commonly used in the an...
research
12/05/2018

Bayesian Spatial Inversion and Conjugate Selection Gaussian Prior Models

We introduce the concept of conjugate prior models for a given likelihoo...
research
07/25/2018

Exponentiated Discrete Lindley Distribution: Properties and Applications

In this article, the exponentiated discrete Lindley distribution is pres...
research
02/03/2021

Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

The paper addresses the problem of estimation of the model parameters of...
research
01/08/2018

Bayesian estimation of a decreasing density

Suppose X_1,..., X_n is a random sample from a bounded and decreasing de...
research
04/30/2020

Bayesian Inference for Johnson's SB and Weibull distributions

The four-parameter Johnson's SB (JSB) and three-parameter Weibull distri...
research
09/09/2019

Fixes to the Ryden McNeil Ammonia Flux Model

We propose two simple fixes to the Ryden and McNeil ammonia flux model. ...

Please sign up or login with your details

Forgot password? Click here to reset