Transformation and weighting in regression
Transformation and weighting in regression
Robust regression and outlier detection
Robust regression and outlier detection
Diagnostics in transformation and weighted regression
Technometrics
Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
Computational Statistics & Data Analysis
Computing least trimmed squares regression with the forward search
Statistics and Computing
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
On testing a subset of regression parameters under heteroskedasticity
Computational Statistics & Data Analysis
Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data
Computational Statistics & Data Analysis
Robust regression diagnostics with data transformations
Computational Statistics & Data Analysis
Case-deletion type diagnostics for calibration estimators in survey sampling
Computational Statistics & Data Analysis
On simultaneously identifying outliers and heteroscedasticity without specific form
Computational Statistics & Data Analysis
Robust joint modeling of mean and dispersion through trimming
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The assumption of equal variance in the normal regression model is not always appropriate. Cook and Weisberg (1983) provide a score test to detect heteroscedasticity, while Patterson and Thompson (1971) propose the residual maximum likelihood (REML) estimation to estimate variance components in the context of an unbalanced incomplete-block design. REML is often preferred to the maximum likelihood estimation as a method of estimating covariance parameters in a linear model. However, outliers may have some effect on the estimate of the variance function. This paper incorporates the maximum trimming likelihood estimation (Hadi and Luceno, 1997; Vandev and Neykov, 1998) in REML to obtain a robust estimation of modelling variance heterogeneity. Both the forward search algorithm of Atkinson (1994) and the fast algorithm of Neykov et al. (2007) are employed to find the resulting estimator. Simulation and real data examples are used to illustrate the performance of the proposed approach.