Related Experiment Video
Updated: Oct 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Bayesian inference for the log-symmetric autoregressive conditional duration model
Jeremias Leão1, Rafael Paixão2, Helton Saulo3
1Departamento de Estatística, Universidade Federal do Amazonas, Campus Senador Arthur Virgílio Filho, Av. General Rodrigo Octávio, 6200, Coronado I, 69080-900 Manaus, AM, Brazil.
Abstract:
This paper adapts Hamiltonian Monte Carlo methods for application in log-symmetric autoregressive conditional duration models. These recent models are based on a class of log-symmetric distributions. In this class, it is possible to model both median and skewness of the duration time distribution. We use the Bayesian approach to estimate the model parameters of some log-symmetric autoregressive conditional duration models and evaluate their performance using a Monte Carlo simulation study. The usefulness of the estimation methodology is demonstrated by analyzing a high frequency financial data set from the German DAX of 2016.
Related Concept Videos
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Sampling Continuous Time Signal
In the...
Assumptions of Survival Analysis
Distributions to Estimate Population Parameter
The Integrated Rate Law: The Dependence of Concentration on Time
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...

