On the Lp-quantiles for the Student t distribution

@article{Bernardi2016OnTL,
  title={On the Lp-quantiles for the Student t distribution},
  author={Mauro Bernardi and Valeria Bignozzi and Lea Petrella},
  journal={Statistics \& Probability Letters},
  year={2016},
  volume={128},
  pages={77-83}
}
View PDF on arXiv
4 Citations
Background Citations
3
Methods Citations
1
Results Citations
1

4 Citations

Inter-order relations between moments of a Student $t$ distribution, with an application to $L_p$-quantiles

This paper introduces inter-order formulas for partial and complete moments of a Student t distribution with n degrees of freedom. We show how the partial moment of order n − j about any real value m…

Bayesian quantile regression using the skew exponential power distribution

Estimation of conditional extreme risk measures from heavy-tailed elliptical random vectors

In this work, we focus on some conditional extreme risk measures estimation for elliptical random vectors. In a previous paper, we proposed a methodology to approximate extreme quantiles, based on…

Visualização exploratória e predição de desempenho de dados educacionais da Universidade de Brasília

In recent decades the amount of educational data collected by universities has increased. With the help of information that can be extracted from this data, universities can improve teaching quality…

References

SHOWING 1-10 OF 49 REFERENCES

Conditional Lp-quantiles and their application to the testing of symmetry in non-parametric regression

Generalized Quantiles as Risk Measures

In the statistical and actuarial literature several generalizations of quantiles have been considered, by means of the minimization of a suitable asymmetric loss function. All these generalized…

Expectiles and M-quantiles are quantiles

Bayesian inference for L$_{p}$--quantile regression models

A collapsed Gibbs--sampler algorithm is proposed to make Bayesian inference on the regression parameters of the L_p--quantile regression model to generalise quantiles and expectiles to account for the whole distribution of the random variable of interest.

Sampling Student’s T distribution – use of the inverse cumulative distribution function

With the current interest in copula methods, and fat-tailed or other non-normal distributions, it is appropriate to investigate technologies for managing marginal distributions of interest. We…

Functional data analysis of generalized regression quantiles

A functional data analysis approach to jointly estimate a family of generalized regression quantiles, including the conditional quantiles and expectiles as special cases, is developed and significantly improves the estimation efficiency.

Nonparametric M-quantile regression using penalised splines

Quantile regression investigates the conditional quantile functions of a response variable in terms of a set of covariates. M-quantile regression extends this idea by a ‘quantile-like’ generalisation…

Bayesian Robust Quantile Regression

This paper proposes an extension of the ALD Bayesian quantile regression framework to account for fat-tails using the Skew Exponential Power (SEP) distribution, which has an additional key parameter governing the decay of the tails, making it attractive for robust modeling of conditional quantiles at different confidence levels.

A Bayesian Nonparametric Approach to Inference for Quantile Regression

We develop a Bayesian method for nonparametric model–based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the…

Quantiles, expectiles and splines