Modelling High Dimensional Paddy Production Data using Copulas

Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...

Modelling Sample Selection Using Copulas

By a theorem due to Sklar, a multivariate distribution can be represented in terms of its underlying margins by binding them together using a copula function. By exploiting this representation, the “copula approach” to modelling proceeds by specifying distributions for each margin, and a copula function. In this article, a number of copula functions are given, with attention focusing on members...

Modelling multi-output stochastic frontiers using copulas

The aim of this work is to introduce a new econometric methodology for multioutput production frontiers. In the context of a system of frontier equations, we use a flexible multivariate distribution for the inefficiency error term. This multivariate distribution is constructed through a copula function which allows for separate modelling of the marginal inefficiency distributions and the depend...

Modelling Copulas: An Overview

Modelling Dependence in High Dimensions with Factor Copulas

This paper presents new models for the dependence structure, or copula, of economic variables, and asymptotic results for new simulation-based estimators of these models. The proposed models are based on a factor structure for the copula and are particularly attractive for high dimensional applications, involving …fty or more variables. Estimation of this class of models is complicated by the l...