Bartlett corrections in heteroskedastic \(t\) regression models
From MaRDI portal
Publication:2576373
DOI10.1016/j.spl.2005.05.025zbMath1076.62064OpenAlexW2061491825MaRDI QIDQ2576373
Gauss M. Cordeiro, Lúcia P. Barroso
Publication date: 27 December 2005
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2005.05.025
Bartlett correctionmaximum likelihood estimateheteroskedastic modellink functiondispersion parameterStudent's \(t\) distribution
Parametric hypothesis testing (62F03) Point estimation (62F10) General nonlinear regression (62J02) Monte Carlo methods (65C05)
Related Items
Small‐sample testing inference in symmetric and log‐symmetric linear regression models, Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions, Bartlett corrections in Birnbaum–Saunders nonlinear regression models, Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors, Variable Selection in Joint Location and Scale Models of the Skew-t-Normal Distribution, Homogeneity diagnostics for skew-normal nonlinear regression models, Heteroscedasticity diagnostics for \(t\) linear regression models, Statistical Diagnostics for Skew-t-Normal Nonlinear Models
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bartlett corrections and bias correction for two heteroscedastic regression models
- A GENERAL METHOD FOR APPROXIMATING TO THE DISTRIBUTION OF LIKELIHOOD RATIO CRITERIA
- A note on improved likelihood ratio statistics for generalized log linear models
- On the level-error after Bartlett adjustment of the likelihood ratio statistic
- On the corrections to the likelihood ratio statistics
- Improved likelihood ratio statistics for exponential family nonlinear models
- Bartlett corrections for generalized linear models with dispersion covariates
- Improved Likelihood Ratio Tests for Dispersion Models