heteroscedastic

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heteroscedastic

(ˌhɛtərəʊskɪˈdæstɪk)
adj
1. (Statistics) (of several distributions) having different variances
2. (Statistics) (of a bivariate or multivariate distribution) not having any variable whose variance is the same for all values of the other or others
3. (Statistics) (of a random variable) having different variances for different values of the others in a multivariate distribution
[C20: from hetero- + scedastic, from Greek skedasis a scattering, dispersal]
heteroscedasticity n
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014
Translations
eteroschedastico
References in periodicals archive ?
A Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was used to remodel the time-dependent variance of the inflation rate, but the stationary properties of the data were not taken into account.
In particular, heteroscedasticity does not allow us to use equation 3 for the computation of [mathematical expression not reproducible] since it assumes a uniform dispersion of the errors.
Furthermore, heteroscedasticity and autocorrelation sometimes lead to incorrect results.
For examining heteroscedasticity, normality of the data, serial correlation and function form of the model, the diagnostic analysis has been conducted.
In case of cross-sectional data, we usually face the problems of Heteroscedasticity. To check the Heteroscedasticity problem, Breusch-Pegan-Godfrey test was applied.
Newey-West standard error test used to resolve the previous problem, Breusch-Pegan-Godfrey test and White test were used to estimate for the existence of Heteroscedasticity. Jarque-bera test is a method that analyzes the normality of the data and error term.
Classical assumption tests consists of normality test, multicollinearity test, and heteroscedasticity test.
Multiple comparison of age groups in bone mineral density under heteroscedasticity. Biomed Res Int 2015;2015:426847.
This estimation is the most appropriate if the nature of heteroscedasticity is unknown (Green 2000).
When confirming the breakdown of the homogeneity assumption of the variances, that is, the presence of heteroscedasticity, the inferences, confidence intervals and hypothesis testing may yield inaccurate results (Deaton, Reynolds, & Myers, 1983).
This is ascertained in the Engle test performed to check for heteroscedasticity in the temperature residuals (see Table 2).
Group-wise heteroscedasticity was checked for using Modified Wald test in the Fixed Effect regression model using STATA.