Questions tagged [heteroscedasticity]

Question 1

I have a linear model with two continuous variables and three categorical variables. Do I need to check homoscedasticity within each level of my categorical variables, or is it sufficient to check ...

Question 2

In my first course on linear regression, I learned the 4 basic assumptions that every textbook teaches: linearity, independence, homoscedasticity, and normality. However, I recently learned about ...

Question 3

GARCH assumes constant drift $\mu$ - this imply $E[e^r]$ won't be constant and jump wildly. And it contradicts the reality, for stock prices $E[S_{t}/S_{t-1}]=E[e^r]$ doesn't jump with each time step. ...

Question 4

Plotting data onto a scatterplot from the U.S. Department of Transportation shows that there is a clear positive linear relationship between % of drivers under age 21 and fatal incidents per 1000 ...

Question 5

Suppose I have a dataset $\{x_i,y_i,\sigma^2_i\}$ where $x_i$ is my independent variable, $y_i$ is my dependent variable, and $\sigma^2_i$ is the observed variance of $y_i$. I know that the random ...

Question 6

Statistical test(s) to help assess if it is necessary to transform a time series (using a Box-Cox transformation) to stabilise the variance before using Hyndman-Khandakar algorithm for automatic ARIMA ...

Question 7

This is a unique modelling situation we are dealing with. There are measurements $y_i$ taken at different times $t_i$. After the measurements are taken, we have experts telling us that at each $y_i$, ...

Question 8

Assume the following situation: Assume you ran a Fisher Oneway-ANOVA (aov() in R) and found that the residuals are normally distributed based on the Shapiro-Wilk ...

Question 9

In R package brms, the gterms have a Gaussian prior. Is it possible to specify the variance for that Gaussian prior as a nonlinear function of covariates?

Question 10

Consider multiple linear regression $$ y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \varepsilon, \quad \varepsilon|X \ \mathop{\sim}\limits^{\mathrm{iid}}\ N(0,\sigma^2). $$ I estimate the covariance ...

Question 11

I have monthly time series $\{y_t\}$ and $\{x_t\}$ (continuous variables) with just over 200 observations. I model $y_t$ conditional on $x_t$ them as follows: $$ y_t = \alpha_1Jan_t + \dots + \alpha_{...

Question 12

What is the intuition behind consistency of heteroskedasticity-consistent (HC) covariance matrix estimators (heteroskedasticity consistent standard errors) such as Eicker-Huber-White (HC0) and other ...

Question 13

I'm analyzing data from a repeated measures study using a mixed-effects linear model. My dependent variable is Y (an eye-tracking parameter: total fixation duration), and I'm investigating the effect ...

Question 14

I have run simple regression analysis for my work (it is about prediction of body fat using medical images; and I have very low sample size - only 21 samples) and also running the test for regression ...

Question 15

To give an overview: I am currently conducting an interrupted time series analysis on daily social media data, focusing on posting frequencies. Initially, I attempted to implement a segmented ...

Stack Exchange Network

Questions tagged [heteroscedasticity]

homoscedasticity for a linear model

In linear regression, what changes when you use robust standard errors to overcome non-constant variance?

Is GARCH assumption on constant drift wrong in log space?

Is it possible to identify this residual pattern as heteroscedastic or homoscedastic?

Handling Heteroscedastic Data with Generalized Linear Models

Statistical test(s) to assess if it is necessary to transform a time series to stabilise the variance?

Regression when errors are provided to you?

Normality of residuals in aov() can be assumed, but test for homoscedacity fail, do I have to recheck normality for each group applying oneway.test()? [duplicate]

Structured prior in brms

Behavior of heteroskedasticity-consistent standard errors under homoskedasticity

Modelling residual autocorrelation and heteroskedasticity in a small sample

Intuition behind consistency of heteroskedasticity-consistent covariance matrix estimators

How to handle heteroscedasticity in a mixed-effects model?

Test regression assumption

ADF and KPSS tests suggests stationarity, but residuals exhibit heteroscedasticity. How to interpret?

Hot Network Questions