10
votes
Accepted
Difference between heteroskedasticity and overdispersion
No, they are not equivalent. In fact, they are quite unrelated.
Heteroskedasticity is when variance differs between "situations". For instance, in a regression task, the variance of the ...
- 117k
7
votes
White's test interpretation
I wouldn't base my choice of model on a test for heteroscedasticity. And I'm not the only one. Here is a quote from the great George Box:
To make the preliminary test on variances is rather like ...
- 110k
6
votes
Hypothesis tests for Rayleigh variables
"Determine to what extent they're different" seems to be more suggesting a problem in estimation rather than testing. I will proceed to discuss tests, but this consideration may suggest you ...
- 278k
4
votes
White's test interpretation
Using GARCH does not sound like a great option, because you have not established presence of autoregressive conditional heteroskedasticity (and you have used the ARCH-LM test that could have indicated ...
- 64.8k
4
votes
Does the use of "robust standard errors" in cluster randomized trials suggest heterskadistic data, implying there is high between-cluster variability?
I don't think you can make that assumption. In my experience, using robust standard errors is one standard way of dealing with clustering, regardless of whether there is heteroscedasticity.
Whether ...
- 110k
3
votes
Homoskedasticity and Collinearity
There really isn't anything saying that these two things are explicitly related. You can have two predictors that are:
Almost perfectly collinear with heterogenous variance
Almost perfectly collinear ...
- 8,655
2
votes
Accepted
What aspects should I test from a fitted GARCH model?
Residuals vs Standardised Residuals. Residuals are the differences between your observed values and the values predicted by your model.
Standardised Residuals in the context of GARCH models are the ...
- 56.9k
1
vote
How can I tell if a clutser-randomised crossover trial has made a unit of analysis error?
The 'two-sided' test indeed means they consider deviations of the test statistic in both the negative and positive sense (lower and upper tail). A two-sided $\alpha=0.05$ is very standard, if you were ...
- 2,051
1
vote
Anova model assumptions. How to go by?
Why do you think you are "going wrong" at all? Sometimes, there isn't homongeneity of variances. What you should do about it depends on various things. You could look at some plots.
Yes, ...
- 110k
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