Questions tagged [sandwich]

Sandwich, or sandwich variance estimation, refers to a method of estimating standard errors from estimating equations that is robust to many model based assumptions. The preferred tag is "robust-standard-error"

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If the normality assumption in the for the GLS estimation fails, would you switch to GEE?

I want a marginal model, ideally fit via GLS. But the normality of residuals doesn't hold. It isn't much skewed, I don't want any transformations. It's just non-normal in shape. Yet still reporting ...
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M-estimator: There is no "of something" in the definition

I see that when talking about estimator, we have "of something", where "something" refers to a fixed parameter. For example, we say that the sample mean is an estimator of the ...
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what does generalized estimating equations with robust standard error mean

I am currently working on logistic regression and came across some articles stating "generalized estimating equations with robust standard error" or "with robust sandwich estimators for ...
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How to compute sandwich estimator with QMLE and Poisson regression in R (using glmrob package)

I am trying to applying a sandwich estimator to a Poisson regression with QMLE in R, where I used the glmrob function. The code I am using looks like: ...
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Do GEE estimator properties (e.g. bias) change depending on the correlation structure?

A logistic regression estimated using a generalized estimating equation with a clustering variable and an independent correlation structure produces the same parameter estimates as an MLE logistic ...
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How to compute the sandwich variance ML estimator in R

I'm currently estimating a DCC-type model by maximum likelihood. Im using the command solnp and it return an object where I can compute the Hessian H evaluated at ...
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Decision between vcovPC and vcovPL (sandwich)

I want to do a linear probability model with clustered errors. The data also has a panel structure. In the R package “sandwich,” there are two functions: vcovPC() ...
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r quantreg - quantile regression with clustered standard errors

I fit a quantile regression using quantreg:::rq on clustered data. I use the Huber sandwich estimator to obtain cluster-corrected standard errors, which is ...
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Robust SE clustered GLM Gamma Log Link to match GEE Robust SE

How do I get the robust standard errors/sandwich variance estimators for GLM using a Gamma family with a log-link to match the robust standard errors from the GEE output? ...
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How to determine if correlated errors are a result of using an incorrect functional form?

Assume you have identified that the errors of a regression model are correlated. How should you determine if this is a sign of using incorrect an functional form? That is, a situation that should be ...
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Why cannot the sandwich SE be used when the Kenward-Roger denominator df are in use?

This question is about mixed models, and the use of both the Kenward-Roger correction (K-R) for small sample sizes and robust (sandwich) estimators for standard errors in the same model. Is it ...
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Variance Estimation for Least Squares with Probability Weights

I'm running a simulation study and finding that the nominal SEs of the estimated coefficients when using weights in lm in R are an underestimate of the simulation SE. I have confirmed that $\hat{\beta}...
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Calculating sandwich estimator

Considering design matrix $X \in \mathbb{R}^{n\times p}$ $(n>p)$ and response $y\in \mathbb{R}^{n}$. The sandwich estimator can be calculated directly using $$(X^TX)^{-1}X^T diag(r^2) X (X^TX)^{-...
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Robust regression with Sandwich estimator

I understand that rlm (robust regression) addresses issues of outliers and influential observations, but does not address heteroskedasticity. I have come to learn ...
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2 answers
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How to estimate robust sandwich standard errors when estimating parameters using optim() in R?

Currently I am using numerical optimization in R via the optim() function to estimate some parameters in a complicated ...
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How to calculate sandwich standard errors for generalized least squares models?

Dependent data can be modeled using covariance structures like compound symmetry, spherical, AR-1, and other. Using generalized least squares, inference can be made on the regression coefficients ...
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Wondering what type of covariance correction for standard errors is better: Hansen-Hodrick or Newey-West?

I am wondering what type of covariance correction for standard errors is better: Hansen-Hodrick or Newey-West? Also, does someone know if StatsModels package that ...
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Different optimal bandwidths of Newey West (1994) in R and STATA

R and STATA gave very different optimal bandwidths for the same data set. It will be greatly appreciated if someone can give me any hint why this happens. Here are two sample codes from R and STATA ...
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Standard Errors with Weighted Least Squares Regression

For OLS, $\hat{\beta} = (X'X)^{-1}X'y$, and $\text{var}(\hat{\beta}) = (X'X)^{-1} X' \sigma^2 I X (X'X)^{-1}$. I can reproduce these "by hand". For WLS, with heteroskedastic errors and weights in ...
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What does setting vcov=sandwich result in? I was expecting that it will result in HC consistent standard errors [closed]

However, when I checked the results using, vcovHC=sandwich, I found different results. Can anyone help me to figure out the difference? ...
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Multinomial tests of heterogeneity using robust standard errors?

I am looking at differences in frequencies of categorical variables collected at different sites, adjusting for stratification factors of age and sex. I would like to use robust standard error ...
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Difference between Quasi-Poisson and Sandwich Covariance

I understand that both methods can be utilized to obtain correct inference in overdispersed Poisson data. What I don't understand is the difference between them: why the analyst would choose one over ...
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4 votes
1 answer
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How to implement the sandwich estimator in a semi-parametric situation?

I am trying to implement a sandwich estimator described in Zhang et al. (2012, p. 1012) in very brief terms. The information they give is not enough for me to understand what has been actually done, ...
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Reference for incremental sandwich covariance from biglm?

I am working on some similar methods to Lumley's biglm wrapper around Miller's AS274 algorithm, and I can't seem to find a reference for his incremental Huber/White ...
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I can't correct the OLS model with heteroskedasticity by the lmtest means

i'm using a selvaggio model to explain the behavior of deposits in a bank's data, and i need to use the estimated parameters, the problem is the heteroskedasticity that i detectect with breusch-pagan ...
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1 answer
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Clustered (grouped) standard errors MLE in R

I'm doing the following maximum likelihood estimation using mle2 function from bbmle package: ...
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Application of Huber-White Variance Estimates in GLMER

I'm currently working on an analysis in R using GLMER mixed-effects model with a logistic regression framework under the lme4 package. I would like to include empirical (Huber–White sandwich) variance ...
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effect estimate without constant variance (r sandwich vcovHC)

For the sandwich package in R, when using vcovHC(model) where model <- lm(Y~X), what gives the effect estimate regarding the ...
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Sandwich Estimator in Maximum Likelihood Estimation of Logit

I am estimating a discrete choice model using mixed logit using Halton Draws. So everything is effectively done with MCMC. The code is written in MATLAB. I am using MATLAB's ...
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2 votes
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How to calculate robust standard error with offset?

I think the default vcovHC in R's sandwich package does not handle offsets in Poisson models. We see this because robust (heteroscedasticity consistent) standard ...
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2 votes
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Why sandwich estimators aren't always used in OLS regression?

I asked before what is the intuition behind sandwich estimators. I must still missing something because I don't understand why sandwich estimators are not always applied to OLS residuals. Can you ...
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3 votes
1 answer
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Robust OLS versus ML with sandwich estimator

If you compare the standard errors of the OLS coefficients with the White correction, versus the ML estimates with the variance estimated with the sandwich estimator, which standard errors do you ...
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Do robust standard errors protect you from proportional odds assumptions?

Cox Proportional Hazards models are traditionally taught alongside proportional hazards assumptions. There is a corresponding test of proportionality. However, if standard errors are calculated from ...
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2 answers
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Clustered data WITHOUT multilevel / GEE model?

I have a data-set with around 700 observations from 12 centres. Although the clustering effect as tested in a random intercept model didn't seem significant, it seems more appropriate to use a ...
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Huber sandwich estimator in quantile regression

I need the description of Huber sandwich estimate method for quantile regression. I found this "a Huber sandwich estimate using a local estimate of the sparsity function". Sparsity function looks ...
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6 votes
1 answer
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Pairwise comparisons for a regression with sandwich estimates (in R)

The question in short I run a regression in R and made a boxplot of the response variable with grouping by one of the predictor variables. On this boxplot I'd like to add some information about the ...
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11 votes
2 answers
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Robust regression inference and Sandwich estimators

Can you give me an example of the use of sandwich estimators in order to perform robust regression inference? I can see the example in ?sandwich, but I don't quite ...
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4 votes
1 answer
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Robust Residual standard error (in R)

I have a question regarding to the concept of robust standard errors. What I found about that topic is, that one can estimate the robust standard error for regression coefficients to eliminate ...
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