# heteroscedastic

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Related to heteroscedastic: Homoscedastic

## heteroscedastic

(ˌhɛtərəʊskɪˈdæstɪk)
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
Translations
eteroschedastico
References in periodicals archive ?
To overcome the shortcomings of the logit model, we developed a heteroscedastic extreme value (HEV) model, a probit model, and a mixed-logit model.
heteroscedastic) so was transformed logarithmically (log) in order to provide a true interpretation (Atkinson and Nevill, 1998; Hopkins, 2000; Hopkins, et al., 2009).
The power decreases with increasing residual error and is almost always above 80% when |RMAX| = 100% in constant error ([[sigma].sub.i] = 25%) and heteroscedastic error ([[sigma].sub.i] = f([C.sub.i])) scenarios (see Supplemental Material, Figure S3).
Data on hatching times for laboratory experiments were log-transformed to fulfil assumptions of ANOVA; however, variances remained heteroscedastic. Therefore, Welch's ANOVA was used for those data, in addition to Tamhane's T2 post hoc test to further determine differences among groups.
(5.) In order to take into account that standard errors may be heteroscedastic, we used a Huber-White sandwich estimator in these equations (StataCorp, 2001).
(6.) The ARCH (Autoregressive Conditionally Heteroscedastic) models, introduced by Engle (1982), are specific non-linear time series models.
(2006) analyse how outliers affect the identification of conditional heteroscedasticity and estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models and show that the properties of some conditional homoscedasticity tests can be distorted.
One of the first approaches to this problem was suggested by Cochran (1), who investigated maximum likelihood (ML) estimates for one-way, unbalanced, heteroscedastic random-effects model.
Dette  considered the problem of testing for the equality of two curves with heteroscedastic errors and fixed design.
Note if the variance parameters A and B are not equal to zero, Case and Shiller (1989) showed the variance of the equation error term in (4) is heteroscedastic and proposed a feasible Generalized Least Squares (GLS) estimator to correct for this problem.
Since forecast errors are generally heteroscedastic, we utilize the Newey-West (1987) procedure to estimate the covariance matrix of equations (1)-(3), correcting for both the f-order serial correlation and heteroscedasticity.
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