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

learn more… | top users | synonyms

0
votes
0answers
25 views

Robust regression in R with robust::lmRob

I am using the lmRob function in R to do my robust regression. In the R documentation of lmRob found here you can set the ...
1
vote
0answers
24 views

linear regression using heteroskedasticity robust standard errors in R

I want to perform an OLS regression on time series data using heteroskedasticity robust standard erros. So far i can come up with this: ...
0
votes
0answers
16 views

Robust Estimator for GARCH in R programming

I just want to know if R programming provides estimator estimation beside QMLE, because I want to compare these estimators' performance toward outliers. What package provides a function for this ...
2
votes
0answers
39 views

Methods to determine reliability of measurements using median and median absolute deviation

I have several datasets containing hundreds of variables, measured at the same time point and with the same method. Some of these variables have been measured more often and assume consistent values. ...
2
votes
0answers
56 views

Estimating means of correlated distributions with long tails

Suppose I have a relatively large number of samples (~1k) drawn from a series (~40) of increasingly long-tailed distributions (going from approximately normal to approximately log-normal). I want to ...
6
votes
1answer
153 views

Robust estimation of kurtosis?

I am using the usual estimator for kurtosis, $\hat{K}=\frac{\hat{\mu}_4}{\hat{\sigma}^4}$, but I notice that even small 'outliers' in my empirical distribution, i.e. small peaks far from the center, ...
0
votes
0answers
31 views

Delete observations for regression modelling

I am implementing robust regression on some data and when i tested the accuracy on a hold set of data, the accuracy was very bad. I have about 500 000 observations for the regression model and i ...
1
vote
0answers
18 views

Proving the consistancy of the MAD

I am trying to prove that the median absolute deviation from the median (MAD), with k=1.4862, is a consistent estimator of the standard deviation.
1
vote
2answers
55 views

How to test the robustness and the performance of a novel classification algorithm

Suppose you have a new algorithm that you want to publish. Are there any best practices and methodologies you usually consider in order to test the robustness and performance of a new method? The ...
1
vote
0answers
31 views

How robust is estimation using sum of powers? [closed]

Minimizing sum of squares for scale and location estimation is optimum wrt efficiency for normal distributions, and doing so but using the sum of absolute values is maximum likelihood estimation ...
2
votes
1answer
63 views

What makes an econometric model robust?

I was reading a paper on robustness (http://econ.ucsb.edu/~doug/245a/Papers/Robustness%20Checks.pdf) and they say: "To determine whether one has estimated effects of interest, $\beta$; or only ...
0
votes
0answers
31 views

Error message with robust regression in R

Whenever I run a robust regression model in R with rlm and with either M or MM methods, I get the following error message: Error: 'lqs' failed: all the samples ...
1
vote
1answer
47 views

Theil-Sen estimation

Is the theil-sen estimation in robust regression only limited to a two dimensional problem or can you use it for more than one indepedent variable as well?
0
votes
0answers
28 views

Robust estimator of mean for skewed data

For heavy-tailed symmetric data, a trimmed mean or other robust estimator of the mean could be a better estimator of the mean than the sample mean. The trimmed mean will be biased for a skewed ...
3
votes
1answer
48 views

Effective sample size of weighted regression

I am doing a basic linear regression with one predictor with some weighting in R, e.g.,: lm(response~explanatory, weights=w, data=mydata) The weights are ...
1
vote
1answer
93 views

Robust OLS verus 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 ...
4
votes
2answers
96 views

Robust regression - a better understanding

I looked at robust regression for the first time today and I am a bit confused, comparing it to something like ordinary least squares and I am not sure if I am on the right track. I read a few ...
0
votes
0answers
17 views

Robust variance estimators

Given $N$ data points in $\mathbb{R}^p$ - some of which are outliers (drawn from a different distribution from the inliers) - what sorts of algorithms have been designed to estimate the robust ...
0
votes
1answer
63 views

F-test formula under robust standard error

I am attempting to write a program that will (among other things) use the F-test in multivariate regression under standard robust errors. I am having trouble finding a specific formula for the ...
0
votes
0answers
16 views

How should I choose WLS weights with categorical IVs?

I have a 2X2 design (2 categorical independent variables (IVs) with 2 levels each), and a single dependent variable (DV). I have 91 samples in total with these cell counts: 30, 19. 20, 22. My data for ...
0
votes
0answers
65 views

Robust regression goodness of fit

I'm using R to compute robust multiple linear regression. I use the command rlm from the package MASS. As psi function I use ...
4
votes
1answer
257 views

What is the minimum viable cell size for 2x2 ANOVA?

I have a 2x2, between-subjects experimental design (2 independent variables (IVs) with 2 levels each) and one dependent variable (DV). My data are unbalanced and an interaction between the IVs seems ...
0
votes
0answers
78 views

Differences between robustness checks and sensitivity analysis

This is a bit of a terminology question, but what is the difference between a robustness check and a sensitivity analysis? For example, if performing analysis to see how sensitive (or robust) a ...
2
votes
0answers
24 views

Is there a generalization of trimean to $n$-th order (central) moments?

I think trimean is the cat's meow. Is there a generalization of this idea to $n$-th order (central) moments? Basically I live in a world where the pain of outliers vastly exceeds the pain of ...
1
vote
0answers
72 views

How do I generate numbers according to a Robust Soliton distribution?

I'm working on project in Matlab which aim is to demonstrate how Luby Transform codes work. I need to generate generation matrix and I need to get some values from Robust Soliton Distribution, can ...
0
votes
0answers
29 views

An alternative form of $L$-estimators

$L$-estimators are based on the ordered observations $X_{(1)} \leq X_{(2)} \leq \ldots \leq X_{(n)}$ of the random sample $X_1, X_2, \ldots , X_n$. The general $L$-estimtor can be written in the form: ...
3
votes
2answers
66 views

Advice for interpolating a model

I'm new in Stack Exchange, so I hope no to be off topic. I'm also new in bioinformatics and I was asked to perform an analysis. Briefly, I have a dataset of 29 cell lines and the IC50 values of a test ...
1
vote
0answers
71 views

Sandwich covariance for robust regression using M estimators for data exhibiting heteroskedasticity

Following the answer and comments on Python Statsmodels Testing Coefficients from Robust Linear Model based on M-Estimators: I'm wondering how the sandwich form of an m estimator's covariance matrix ...
4
votes
1answer
104 views

The Effect of Outliers

The following question comes up in robust statistics. There are two formula indicated below that I do NOT know how to derive. However, in order to make the context clear, let's start with the easiest ...
4
votes
1answer
198 views

Is a weighted $R^2$ in robust linear model meaningful for goodness of fit analysis?

I estimated a robust linear model in R with MM weights using the rlm() in the MASS package. `R`` does not provide an $R^2$ value ...
4
votes
0answers
62 views

Can I use weights generated by robust regression in a quasipoisson glm in R?

I have response variable count data that should be treated as quasipoisson or something similar. This data also contains outliers which are important to the dataset. I cannot find an r package that ...
2
votes
1answer
70 views

Heteroskedasticly Consistent Estimators for Var-Cov Matrix, Large Sample OLS Regression

I have a cross-sectional data sample of nearly 40,000 observations and tests for heteroskedasticity fail to reject the assumption of homoskedasticity. However, it seems common practice to report ...
3
votes
1answer
71 views

Consistent, non-parametric, robust (to fat tails) estimation of expected value of an asymmetric distribution

Question: Is anyone aware of a consistent, non-parametric estimator of the expected value of an asymmetric distribution that is robust to fat tails? What if we constrain ourselves to the class of ...
2
votes
0answers
36 views

How to find Influence function?

Derive $IF(x;T,F)$ when $$\displaystyle T(F)=\int_{F^{-1}(\alpha)}^{F^{-1}(1-\alpha)}x ~dF(x)$$ Here $IF$ stands for Influence function. Trial: Here $$\begin{align}IF(x;T,F) &=\lim_{t\to ...
18
votes
5answers
519 views

What would a robust Bayesian model for estimating the scale of a roughly normal distribution be?

There exists a number of robust estimators of scale. A notable example is the median absolute deviation which relates to the standard deviation as $\sigma = \mathrm{MAD}\cdot1.4826$. In a Bayesian ...
1
vote
1answer
23 views

Repeat normality check after modifying group cut-off?

I have a set of data which consists of one independent variable (2 groups) and one dependent variable. I successfully checked for normality (each group separately) and conducted a t-test. Now I want ...
4
votes
2answers
82 views

What would be a parametric model with properties similar to the Theil-Sen estimator?

The Theil-Sen estimator is a really nifty algorithm that produces a regression line that is relatively insensitive to outliers both in the response variable and the predictor variable. I've been ...
1
vote
0answers
39 views

Iteratively reweighted least squares : asymmetric weights

For robust m-estimation, all the convergence results I'm aware of assume symmetric weights (eg: Huber function) in their formulation of the iterative reweighted least squares algorithm. Does the ...
3
votes
1answer
190 views

Quantile regression vs. Li's regression: which should I use, and when?

Is there a general rule of thumb about when robust regression or quantile regression is preferred in the presence of outliers? For example, I have a dataset where the DV exhibits extreme positive ...
0
votes
1answer
74 views

Robust test for time series count data

I'm going to analyse suicide rates for a time series, and I'd like to use robust tests, but I don't know which would be a good one. My purpose is to compare the variation of the suicide rates through ...
0
votes
0answers
40 views

Is there a method for testing follow-up individual factor and item invariance using the lava an or semTools packages in R?

I've found the "measurementInvariance" command from the semTools package EXTREMELY helpful recently, but now I am wondering if there is a way to conduct follow-up factor-level and item-level ...
0
votes
1answer
89 views

Inspecting assumption of homoscedasticity

Using a Fligner test to infer about the respect of the assumption of homoscedasticity is not very smart given that the Fligner test tests to the null that there is no difference of variance between ...
2
votes
1answer
151 views

Robust regression and Sandwich estimators

Can you give me an example of the use of sandwich estimators in order to perform a robust regression? I can see the example in ?sandwich but I don't quite ...
4
votes
1answer
2k views

Error “system is computationally singular” when running a glm

I'm using the robustbase package to run a glm estimation. However when I do it, I get the following error: ...
0
votes
1answer
74 views

Robustly standardize residuals in MM regression

Does anyone know how we can robustly standardize the residuals in MM regression? First we perform MM regression and then obtain the residuals: how can we robustly standardize the residuals obtained ...
2
votes
2answers
196 views

When is the median more affected by sampling error than the mean?

I'm writing a paper on making probability estimates, and it's been asserted to me that I should take the median of the estimates given by my participants, rather than the mean. I've been told I should ...
1
vote
0answers
27 views

What is the statistical efficiency of L-moments?

In particular I am interested in the scale estimator. Hopefully it is much better than that of IQR.
1
vote
0answers
13 views

F test for regressions with a small N with robust standard errors [duplicate]

I have a binary dummy with only a very small number of values for this dummy set to "1" (say 7 out of 120). When I run the regression the F-test using robust standard errors, the f-test does not show ...
5
votes
3answers
188 views

Mean has lower standard error than 5% trimmed mean?

I'm investigating using a trimmed mean to measure the location of various distributions. The distributions sometimes are heavily contaminated and sometimes not. Usually they follow something similar ...
4
votes
1answer
120 views

Does inference from a heteroskedasticity-consistent covariance matrix follow the t-distribution or the normal?

Background I'm currently looking into how reliable confidence intervals are on bounded scores (EQ-5D) and I compare regular asymptotic, robust and botstrap-based confidence intervals. The robust ...