Loss based prior for the degrees of freedom of the Wishart distribution

03/24/2021
by   Sotiris Prevenas, et al.
0

In this paper we propose a novel method to deal with Vector Autoregressive models, when the Normal-Wishart prior is considered. In particular, we depart from the current approach of setting ν=m+1 by setting a loss-based prior on ν. Doing so, we have been able to exploit any information about ν in the data and achieve better predictive performances than the method currently used in the literature. We show how this works both on simulated and real data sets where, in the latter case, we used data of macroeconometric fashion as well as viral data. In addition, we show the reason why we believe we achieve a better performance by showing that the data appears to suggest a value of ν far from the canonical m+1 value.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/03/2019

The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations

This work investigates the effects of using the independent Jeffreys pri...
07/20/2018

On a Loss-based prior for the number of components in mixture models

We propose a prior distribution for the number of components of a finite...
06/29/2021

Predictive Model Degrees of Freedom in Linear Regression

Overparametrized interpolating models have drawn increasing attention fr...
09/05/2012

Restricting exchangeable nonparametric distributions

Distributions over exchangeable matrices with infinitely many columns, s...
03/24/2014

Simultaneous sparse estimation of canonical vectors in the p>>N setting

This article considers the problem of sparse estimation of canonical vec...
10/27/2020

Nonlinear Monte Carlo Method for Imbalanced Data Learning

For basic machine learning problems, expected error is used to evaluate ...
03/21/2019

Feature quantization for parsimonious and interpretable predictive models

For regulatory and interpretability reasons, logistic regression is stil...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.