# Log-symmetric quantile regression models

arXiv: https://arxiv.org/abs/2010.09176 · HTML: https://mlsanity.com/paper/2010.09176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Parametric quantile autoregressive conditional duration models with application to intraday value-at-risk](https://mlsanity.com/paper/2308.15571/) (2023)

## Abstract (arXiv)

Regression models based on the log-symmetric family of distributions are particularly useful when the response is strictly positive and asymmetric. In this paper, we propose a class of quantile regression models based on reparameterized log-symmetric distributions, which have a quantile parameter. Two Monte Carlo simulation studies are carried out using the R software. The first one analyzes the performance of the maximum likelihood estimators, the information criteria AIC, BIC and AICc, and the generalized Cox-Snell and random quantile residuals. The second one evaluates the performance of the size and power of the Wald, likelihood ratio, score and gradient tests. A real box office data set is finally analyzed to illustrate the proposed approach.