In statistics, heteroskedasticity (or heteroscedasticity) happens when the standard errors of a variable, monitored over a specific amount of time, are non-constant. With heteroskedasticity, the ...

We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the ...

(See "Heteroscedasticity and Normality Tests" for details.) The p -values for the test statistics are given in parentheses. These tests strongly indicate heteroscedasticity, with p < 0.0001 for all ...

The basic ARCH (q) model (p =0) is a short memory process in that only the most recent q squared residuals are used to estimate the changing variance. The GARCH model (p >0) allows long memory ...

This is a preview. Log in through your library . Abstract Diagnostic tests and plots are proposed for detecting heteroscedasticity in nonparametric regression. The large and small sample power ...

Zhou Zhou, Heteroscedasticity and Autocorrelation Robust Structural Change Detection, Journal of the American Statistical Association, Vol. 108, No. 502 (June 2013), pp. 726-740 ...

Contamination and heteroscedasticity greatly perturb these estimators (particularly the quantiles and expectiles) in the tails. Extremiles present the smallest mean absolute errors in cases of ...