Refers to the property of a random process to have non-constant variance along some continuum. This most commonly presents in regression where the error variance increases as a function of one or more predictors, but also commonly refers to a time series whose variance changes over time.
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6answers
1k views
When conducting a t-test why would one prefer to assume (or test for) equal variances rather than always use a Welch approximation of the df?
It seems like when the assumption of homogeneity of variance is met that the results from a Welch adjusted t-test and a standard t-test are approximately the same. Why not simply always use the Welch ...
11
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4answers
1k views
Checking ANOVA assumptions
A few months ago I posted a question about homoscedascity tests in R on SO, and Ian Fellows answered that (I'll paraphrase his answer very loosely) "homoscedascity tests are not a good tool when ...
11
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2answers
3k views
Transforming proportion data: when arcsin square root is not enough
Is there a (stronger?) alternative to the arcsin square root transformation for percentage/proportion data? In the data set I'm working on at the moment, marked
heteroscedasticity remains after I ...
9
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2answers
455 views
Measures of residuals heteroscedasticity
This wikipedia link lists a number of techniques to detect OLS residuals heteroscedasticity. I would like to learn which hands-on technique is more efficient in detecting regions affected by ...
8
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3answers
524 views
Regression modelling with unequal variance
I would like to fit a linear model (lm) where the residuals variance is clearly dependent on the explanatory variable.
The way I know to do this is by using glm with the Gamma family to model the ...
8
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2answers
1k views
What are the dangers of violating the homoscedasticity assumption for linear regression?
As an example, consider the ChickWeight data set in R. The variance obviously grows over time, so if I use a simple linear regression like:
...
8
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3answers
948 views
Advice on explaining heterogeneity / heteroscedasticty
I am looking for any help, advice or tips in how to explain heterogeneity / heteroscedasticity to biologists in my department. In particular I want to explain why its important to look for it and deal ...
8
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2answers
4k views
Linear regression, heteroscedasticity, White's test interpretation?
I am trying to test whether my regression has an issue of heteroscedasticity. After running a regression, I can clearly see that the residual plot has a pattern. After taking a log of the dependent ...
8
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1answer
158 views
What to do with heterogeneity of variance when spread decreases with larger fitted values
I am trying to produce a linear mixed model the R code is as follows.
lme(Average.payoff~Game+Type+Others.Type+Game:Type+Game:Others.Type+Type:Others.Type,random=~1|Subjects,method="REML", ...
7
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3answers
5k views
Interpretation of the p-values produced by Levene's or Bartlett's test for homogeneity of variances
I have run Levene's and Bartlett's test on groups of data from one of my experiments to validate that I am not violating ANOVA's assumption of homogeneity of variances. I'd like to check with you guys ...
7
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2answers
706 views
Whether to use robust linear regression or bootstrapping when there is heteroscedasticity?
I have a dataset where I need to do linear regression. Unfortunately there is a problem with heteroscedasticity. I´ve rerun the analysis using robust regression with the HC3 estimator for the variance ...
7
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2answers
844 views
Brown-Forsythe and Welch f-ratios in two-way ANOVAs?
I understand that in One-Way ANOVA two alternative F-Ratios have been derived to be robust when homogeneity of variance has been violated. Tomarkin and Serlin (1986) review amongst other techniques ...
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2answers
6k views
Spearman's or Pearson's correlation with Likert scales where linearity and homoscedasticity may be violated
I am wanting to run correlations on a number of measurements where Likert scales were used. Looking at the scatterplots it appears the assumptions of linearity and homoscedasticity may have been ...
6
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1answer
2k views
Calculate Newey-West standard errors without an lm object in R
I asked this question yesterday on StackOverflow, and got an answer, but we agreed that it seems a bit hackish and there may be a better way to look at it.
The question: I would like calculate the ...
5
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2answers
646 views
What does having “constant variance” in a linear regression model mean?
What does having "constant variance" in the error term means. As I see it , we have a data with variable and 1 independent variable. This is one assumption of linear regression. I am wondering what ...
5
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2answers
2k views
When to use (non)parametric test of homoscedasticity assumption?
If one is testing assumption of homoscedasticity, parametric (Bartlett Test of Homogeneity of Variances, bartlett.test) and non-parametric (Figner-Killeen Test of ...
5
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1answer
185 views
Is bootstrapping standard errors and confidence intervals appropriate in regressions where homoscedasticity assumption is violated?
If in standard OLS regressions two assumptions are violated (normal distribution of errors, homoscedasticity), is bootstrapping standard errors and confidence intervals an appropriate alternative to ...
5
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2answers
52 views
Homogeneity testing of baseline characteristics in medical trials
I have been reading medical journals and they repeatedly show baseline characteristics of samples from a randomised controlled trial, which they have then tested to ensure no differences between the ...
5
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2answers
229 views
Measuring homogeneity across different spatial aggregations of data
I'm working with dataset of individual households that I aggregate into 'areas' using several different spatial configurations, from smaller to bigger.
These areas are then characterized by four ...
5
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1answer
66 views
Constant variance assumption in regression model
My question is how do we check the constant variance assumption in a regression model?
5
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1answer
174 views
Inference in linear model with conditional heteroskedasticity
Suppose I observe independent variable vectors $\vec{x}$ and $\vec{z}$ and dependent variable $y$. I would like to fit a model of the form:
$$y = \vec{x}^{\top}\vec{\beta_1} + \sigma ...
5
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1answer
145 views
Linear regression with shot noise
I'm looking for the right statistical terminology to describe the following problem.
I want to characterize an electronics device that has a linear response
$Y = \beta_0 + \beta_1 X + \epsilon$
...
5
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1answer
191 views
Sandwich estimator intuition
Wikipedia and the R sandwich package vignette give good information about the assumptions supporting OLS coefficient standard errors and the mathematical background of the sandwich estimators. I'm ...
5
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1answer
131 views
Conceptual distinction between heteroscedasticity and non-stationarity
I'm having trouble distinguishing between the concepts of scedasticity and stationarity. As I understand them, heteroscedasticity is differing variabilities in sub-populations and non-stationarity is ...
5
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2answers
127 views
Quantile regression and heteroscedasticity/autocorrelation
I hear it said [1] that QR makes no distribution assumptions about its error term.
Question 1: Does this mean that heteroscedastic and serially correlated disturbances do not effect the ...
4
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2answers
4k views
How to run two-way ANOVA on data with neither normality nor equality of variance in R?
I am working on my master thesis at the moment and planned on running the statistics with SigmaPlot. However, after spending some time with my data I came to the conclusion that SigmaPlot might not be ...
4
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3answers
6k views
Autocorrelation and heteroskedasticity in panel data
In the research, both autocorrelation and heteroskedasticity are detected in panel data analysis. I can solve them separately in stata with command "xtregar" and "robust", respectly. However, I cannot ...
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3answers
97 views
Why it is natural to expect equality of variances?
When conducting various statistical test why do we expect equality of variances/ homoscedasticity/sphericity etc.?
4
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1answer
216 views
Is there an exact version of marginal homogeneity test?
Here's what I know:
I have read the chapter (p347ff) in Agresti, 1990, regarding dependent two-way tables, and I believe I understand the basics. My problem is that Agresti's model-based approaches ...
4
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3answers
732 views
Can you use heteroskedastic time series variables within a regression model?
We are working on a multivariate linear regression model. Our objective is to forecast the quarterly % growth in mortgage loans outstanding.
The independent variables are:
1) Dow Jones level.
2) % ...
4
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1answer
184 views
(Quantile regression) Which standard error for heteroscedasticity & serial correlation
I have heteroscedastic and autocorrelated residuals in my multivariate quantile regression model.
What's the quantile regression standard error estimator that's robust to this? Something hopefully ...
4
votes
2answers
356 views
What is the Bayesian counterpart to a two-sample t-test with unequal variances?
I am looking for the bayesian counterpart of the two-sample t-test with unequal variances (the Welch test). I am also looking for a multivariate test, like Hotelling's T statistic. References ...
4
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1answer
786 views
Calculating probability for bivariate normal distributions based on bootstrapped regression coefficients
Dear all,
I was encouraged to ask this question here as well as on stackoverflow and would be very appreciative of any answers...
Due to hetereoscedasticity I'm doing bootstrapped linear regression ...
4
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1answer
157 views
Is there a better way to create variables with a certain correlation and one of them is heteroskedastic?
My goal is to generate two variable which are correlated and one of them is heteroscedastic with regards to an grouping variable.
To create two variables with a desired correlation the common way to ...
4
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2answers
347 views
Heteroskedasticity and standard deviation
I am looking at state-wide data (entire population) of a school's grade as a function of the school's poverty index. The data appears to me to be an unconditional heteroskedastic distribution. I am ...
4
votes
1answer
111 views
Modelling both mean and dispersion of count data
I have a model of the following form:
$P(Y \mid X) = \,D(\mu,\sigma^2) ~~\text{where}$
$\mu = f(X) ~~\text{and}~~ \sigma^2=g(X)$
where $y$ is the response vector of count data, $X$ is the predictor ...
4
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1answer
166 views
Is there a method to check the homogeneity of variance of a single time series?
I have a data frame with prices/dates, is there a method that checks if the variance is homogeneous during all the series? I know there are many test like fligner.test, bartlett.test but I need to ...
4
votes
2answers
204 views
ANOVA for non-normal heterogenous unequally replicated data
I need some advice on how to proceed with my data analysis. I have 3 groups (Archaea, Bacteria and Eukarya). Each group has unequal number of individual species (70, 651, and 244 respectively). Each ...
4
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0answers
70 views
Regression model with heteroskedasticity in both variables
I've been learning (lurking) from this site for a while and I finally have a question I haven't seen answered yet.
I'm doing a flight test and trying to fit the resulting data to linear line. From a ...
4
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0answers
286 views
How to correct for heteroskedasticity in fixed effects panel regression with correction for clustered standard error?
I am a student at RSM and I have a question regarding my regression analysis for my thesis as I have encountered issues I do not know how to deal with.
I have performance data (dependent variable) of ...
3
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3answers
2k views
Why is the Breusch-Pagan test significant on simulated data designed not to be heteroscedastic?
I'm testing the residuals of a linear regression using Breusch-Pagan Test to detect Heteroscedasticity.
This is the plot of the residuals:
and this is the R code:
...
3
votes
1answer
362 views
Do these residual plots indicate that my least squares regression coefficient estimates may be biased?
Lets say I have a linear regression: $$y \sim 1 + x_1+x_2$$
where the range of $x_2$ is $[0,10]$. I fit this model using lm or ...
3
votes
3answers
1k views
Linear model Heteroscedasticity
I have the following linear model:
To address the residuals heteroscedasticity I have tried to apply a log transformation on the dependent variable as $\log(Y + 1)$ but I still see the same fan ...
3
votes
2answers
741 views
Kruskal-Wallis or Fligner test to check homogeneity of variances?
I have a doubt, I need to check the homogeneity of variances on the residuals of a linear regression. I read that Kruskal is also good without assuming a normal-distribution. But I don't know if it's ...
3
votes
2answers
348 views
Model errors, residuals and heteroscedasticity
I have a quick question about the correct way to describe variance functions when seeking to cope with heteroscedasticity. As I understand it the statistical error of a model represents the departure ...
3
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2answers
4k views
How do I interpret the results of a Breusch–Pagan test?
In R I can perform a Breusch–Pagan test for heteroscedasticity using the ncvTest function of the ...
3
votes
1answer
108 views
QQ plot is consistent with normality when subgroups are non-normal
I have read that for a one way ANOVA, you should check that the model residuals are normally distributed. If the variance of each group is homogeneous then this implies that the residuals with each ...
3
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1answer
358 views
Are significance tests for the assumption of constant variance too strict when sample size is large?
My question is regarding linear regression and non-constant variance. I've heard that even though a large data set fails a normality test, this does not necessarily mean the data is not normal. This ...
3
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1answer
322 views
How can these residuals have homogeneity of variances?
I'm testing the Homogeneity of Variances with Fligner-Killeen test using the function fligner.test in stats
On the chart below I have plotted the residuals that pass this test ...
3
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2answers
1k views
Why do I get different heteroscedasticity robust standard errors in R when using the plm package?
I am now writing my bachelors thesis and I have come across some difficulties. I am about to do some panel regressions with time and entity fixed effects and I would therefore like to use the plm ...
