Object-oriented Computation of Sandwich Estimators

By Achim Zeileis. 2006

665 citations recorded by the source corpus.

Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a414d7ddcb0e78067be8cd335462263962ba584d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Econometric Computing with HC and HAC Covariance Matrix Estimators (2004, neutral): “This is implemented in the function NeweyWest(lmobj, lag = NULL, ...) where lag speciﬁes L and ... are (here, and in the following) further arguments passed to other functions, detailed information is always available in the reference manual.”
Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020, methodology): “Here, the conceptual building blocks from Zeileis (2006b) are brieﬂy repeated and then carried further for clustered covariances.”
Homophony and morphology: The acoustics of word-final S in English 1 (2015, methodology): “…the same word type may also arise from more general processes of [5] For the comparisons the packages multcomp (Hothorn et al. 2008) and sandwich (Zeileis 2004, 2006) were used, implementing the specific procedure suggested by Herberich et al. (2010) for scenarios with unbalanced group sizes,…”
Simultaneous Inference in General Parametric Models (2008, neutral): “…to compute some alternative covariance estimator S n as follows: R> amod_glht_sw - glht(amod, linfct = mcp(alength = "Tukey") We used the sandwich() function from package sandwich (Zeileis, 2004, 2006) which provides us with a heteroscedasticity-consistent estimator of the covariance matrix.”
Beta Regression in R (2010, methodology): “For further details see Zeileis (2006a).”
Extracting, Computing and Exploring the Parameters of Statistical Models using R (2020, methodology): “However, additional features that do not belong to the core functions of parameters require the installation of other packages, such as sandwich (Zeileis, 2006) for robust estimation, psych (Revelle, 2019) for factor analysis or PCA or cAIC4 (Saefken, Ruegamer, Kneib, & Greven, 2018) for parameter…”
Regression Models for Count Data in R (2008, methodology): “In R , this estimator is provided by the sandwich() function in the sandwich package (Zeileis 2004, 2006).”
Gaussian Copula Marginal Regression (2012, methodology): “For comparison, we also report the corresponding estimates using the independence likelihood with standard errors computed from the sandwich heteroskedasticity and autocorrelation consistent estimator for time-series data (Zeileis, 2006) to account for the serial dependence.”
Dose-Response Analysis Using R (2015, methodology): “Standard errors, which are robust against misspecification of the distributional assumptions, may easily be obtained by means of the packages lmtest and sandwich as shown in Example 2 in S1 File [41, 42].”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, methodology): “CRSEs are computed using the sandwich (Zeileis, 2006) package.”