The assumptions of logit model are relaxed progressively in the heteroscedastic
, probit, and mixed-logit models.
Duncan, Estimating heteroscedastic
variance in linear models.
which shows that an additional term is needed in the regression, especially if the random error [epsilon] is heteroscedastic
Parametric ANCOVA and the rank transform ANCOVA when the data are conditionally non-normal and heteroscedastic
However, using hypothesis tests on data that are skewed, contain outliers, are heteroscedastic
, or have a combination of these characteristics (raw RT data typically have at least the first two) reduces the power of these tests and can result in a failure to detect a real difference between conditions (Wilcox, 1998).
Generalized autoregressive conditional heteroscedastic
(GARCH) models have frequently been applied to the analysis of financial time series (Engle et al., 1987; De Jong et al., 1992; Kodres, 1993; Vlaar & Palm, 1993).
--Heteroscedastic ordered probit: The ordered probit imposes an equal variance in residual happiness, whereas the heteroscedastic
ordered probit allows both the mean and the variance of happiness to vary by country-year.
To segregate the effect of inputs on mean and variance of output, a heteroscedastic
production function featuring flexible risk effects is suggested; where the variance of the stochastic error term is allowed to vary with levels of managed inputs [Just and Pope (1978, 1979); Anderson and Griffiths (1981)].
However, none of the outcome variables were normally distributed, and variance of the error term was not constant (i.e., heteroscedastic
), so all models were estimated by using the Huber--White covariance matrix (15).
Estimation of heteroscedastic
variances in linear models.
"Robust Estimation in Heteroscedastic
Linear Models." Annals of Statistics, 10(2): 429-441.