# Questions tagged [robust]

Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).

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### How does Huber compute the $\operatorname{var}(s_n)/E[s_n]^2$ and $\operatorname{var}(d_n)/E[d_n]^2$?

(N.B. I am cross posting this question from math stackexchange since after x days I have still not received any responses.) How does Huber in book 'Robust statistical procedures' in chapter 1 compute ...
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### Interpet coefficient estimates with log(Y) in rdrobust package

1.I want to interpet the coefficients of RDD model above. The dependent variable is log-transformed. Should it be the same as linear regression even though I am using local polynomial regression with ... 1 vote
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### How to implement an S-estimator

I'm trying to implement an MM-estimator in python. I have a working implementation of an M-estimator statsmodels.RLM - which is implemented as an iteratively re-weighted least squares algorithm. I am ...
1 vote
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### Effect of robust maximum likelihood estimator in structural equation modelling when data is normal?

I understand that when data are nonnormal robust maximum likelihood estimator can be used. I'm wondering are there any disadvantages of using a robust maximum likelihood estimator when the data are ...
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### Structural equation modeling (latent growth models): robust estimators to handle outliers?

Can I use robust estimators (e.g., "MLM" and "MLR"estimator lavaan options) to overcome outliers within my sample, or should I remove outliers? For context, I am modelling the ...
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### How are robust standard errors applied in logistic regression

I have been reading up on robust standard errors and had a few questions regarding how their use in logistic regression. I have read here that heteroscedasticity is not an issue in logistic regression ...
1 vote
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### Robustness check in Structural Equation Modelling (SEM) in R

I have conducted SEM analysis in R and used Maximum Likelihood Robust estimator as my data are categorical and deviate from multivariate normality. when I submitted my manuscript, one reviewer asked ... 1 vote
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### Influence function of the IQR

In class I am told the influence function of the IQR should be a constant times the difference between the influence function for the 75th percentile and the influence function for the 25th percentile,...
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### How do you use ordinal response data with several random effects to do robust hypothesis testing?

I want to explore the roles of lung presence and habitat on tadpoles' ability to tolerate low oxygen levels. My experimental design generated several measurements of "responsiveness" (on an ...
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### How can I estimate robust standard errors for robust estimators? R

I am doing a robust regression and I want to estimate robust standard errors for my regression. I do know how to do a robust regression in R and how to estimate a robust standard errors for a ...
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### How do the "C" step and robust weighting work in the FASTMCD algorithm for robust covariance estimation?

I am reading up on the FastMCD algorithm [https://arxiv.org/pdf/1709.07045.pdf#page=2] hoping to better understand its implemenation. I think I follow the high level concept for MCD Its basically the ...
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### Robust estimators for count data

I am looking for robust estimators for parameters of a processes producing count data: $$(n_1,...,n_K), n_i\in\mathbb{N}$$ that is the underlying distribution is something like Poisson or Negative ...
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### Robust Optimization: Minimizing $\mathbb{E}[X]+\alpha\sigma(X)$ vs. $\mathbb{E}[X]+\alpha\textbf{Var}(X)$

In robust optimization it is common to minimize the objecive $\mathbb{E}[X]+\alpha\sigma(X)$ instead of the expectation. Would it also be sensible to minimize $\mathbb{E}[X]+\alpha\textbf{Var}(X)$ ...
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### Robustness of Quantile Regression

Is the 99th Quantile Regression model a robust model? From my understanding, Quantile Regression is supposed to be robust in nature, but removing some outliers using IQR, the results obtained by 99th ...
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### Distributionally Robust Optimization: how to know distributions in ambiguity set?

In distributionally robust optimization, it is said that ambiguity sets are created from empirical sample distribution such that the distributions within the ambiguity set 'Q' are at certain ...
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### Logistic regression via robust glm (glmrob) not appropriate if have only one observation in one of two categories of an independent variable?

I received abnormal results when using glmrob (R function from robustbase) when assessing the association of a binomial independent variable X0 with a binomial dependent variable Y using logistic ...
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### Robust options to fit GLM or GAM for overdispersed Poisson counts (quasipoisson or negative binomial) in R

It appears glmrob from the robustbase library does not support quasipoisson or ...
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### Error in solve.default(pdMatrix(a, factor = TRUE)) : system is computationally singular: reciprocal condition number

I am performing a linear mixed-effect model. ...
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### Robust Beta Regression in R

I have been trying to fit a beta regression to a percentage/proportion outcome variable. I used libraries as glmmTMB, gamlss, ...
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### With what goodness measures can cross-validation be done for robust regressions?

Cross-validation involves splitting data in training and test sets (several times) and assessing the goodness of applying the fitted coefficients from the training set to the test set. I was wondering ...
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### How to use the bwwtrim function of the WRS package? (robust 3-way mixed ANOVA)

in our study, we look at the effects of typical/enhanced body checking on eating pathology before and after the intervention, depending on the level of body concern (see below). Since the assumptions ...
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### Do I need to test for autocorrelation or normality assumption if I am running the regression with standard errors?

I used OLS regression to estimate a relationship between X and Y with a couple of control variables. However, when I tested for heteroskedasticity with Breusch–Pagan/Cook–Weisberg test, my residuals ...
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### how well would a robust mixed model fit these data? R (rlmer)

I want to investigate Y ~ X1 * X2 + (1|ID on this dataset (there's a plot of these data in that post too, it's the same dataframe) Y is a continuos outcome ...