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
17 views

Simulate different types of outliers (with R) in a linear regression?

I'm trying to simulate a regression model with outliers to implement and understand more deeply the robust regression. I tried using a mixture between normal errors and uniforms.But as you can see, ...
2
votes
1answer
17 views

Are there any models that can handle out of sample features?

So I'm facing a regression problem where I have a categorical features (factor) where the levels are very commonly different between the training set and the test set. I have multiple measurements ...
0
votes
0answers
14 views

Kruskal-wallis v ANOVA v robust methods

I have data from 3 groups which don't follow a normal distribution, my sample sizes are ~600 for the control group and ~300 for the test ones. I made a density plot of the data which looks like: ...
1
vote
0answers
49 views

how to assess importance of each predictor in robust linear regression

I have been using rlm() in the MASS library in R with the redescending weights (using MM or Tukey's biweight function). I wanted to find the importance of each predictor in the fitted model. Can ...
0
votes
0answers
21 views

Alternative to linear regression

I need to run hundreds of linear regression models, with the same set of independent variables, but with varying dependent variables. I have checked normality for a few dozens. Some are normally ...
5
votes
2answers
110 views

Using HAC standard errors although there might be no autocorrelation

I'm running a couple of regressions and, as I wanted to be on the safe side, decided to use HAC (heteroskedasticity & autocorrelation consistent) standard errors throughout. There might be a few ...
0
votes
0answers
12 views

Does Huber regression account for autocorrelated errors?

It seems that regression using the Huber penalty accounts for deviations from the Gaussian distribution in the residuals - but what about serial correlations?
0
votes
0answers
20 views

Robust one sample tests of variance or scale

A common one sample test for variance is the chi-square test, e.g., http://www.itl.nist.gov/div898/handbook/eda/section3/eda358.htm. What are some robust testing alternatives for variance when the ...
0
votes
0answers
10 views

Robust MM regression did not fit well and followed the pattern of first predictor

I have been using the rlm (MM estimator) command form the MASS package in R. Unfortunately the pattern of fit.model followed the ...
1
vote
0answers
22 views

Robustness of Gaussian Copula

I am looking for studies regarding the robustness of a multivariate Gaussian copula. Specifically I am wondering whether estimates of the dependence parameter in a multivariate Gaussian Copula (Sigma) ...
1
vote
0answers
21 views

Robust estimation of multivariate reference bands

I have a subjectivly healthy population of approx 1200 individuals with three measurements on the continous scale, we can call them y1, y2 and y3. All of these are strongly related to age in a non-...
1
vote
1answer
38 views

Comparison of Stahel-Donoho and Minimum Covariance Determinant estimation

I am interested in detecting multivariate outliers in a low dimensional data set ($n<p$). Various high-breakdown robust methods for multivariate settings such as Stahel-Donoho and Minimum ...
0
votes
0answers
6 views

Comparison between default, robust unclustered and cluster robust standard errors

I regress from my data lnhr on lnwg, first with the default OLS (POLSiid), second with the robust unclustered option (POLShet), third with the cluster robust option (POLSpanel). I understand with the ...
3
votes
2answers
95 views

Robust methods and penalized regression

Are penalized regression methods such as ridge or lasso sensitive to outliers? If so, what options are there in regards to robust methods for penalized regressions and are there any packages in R?
0
votes
0answers
23 views

Heteroskedasticity consistent SEs with Proc ROBUSTREG in SAS

We are using the Proc ROBUSTREG command in SAS to down-weight the influence of the outliers (mainly in the Y-direction). Furthermore, we wish to use heteroskedasticity consistent SEs and standardized ...
0
votes
0answers
39 views

PROC ROBUSTREG or PROC NLMIXED in SAS, to down-weight effects of non-normality and outliers

We are conducting an OLS regression in SAS. We have performed all diagnostic tests and concluded that there is non-normality in the residuals, influential outliers and heteroskedasticity. We would now ...
0
votes
2answers
87 views

Best regression correcting for non-normality, outliers and heteroskedasticity

We are performing a regression on cross-sectional data for $Y$ = subjective well-being (scale 0-10) and $X$ = working hours (divided into 5 dummy categories; less than 27 hours, 27-32 hours etc). ...
4
votes
1answer
49 views

Generating random samples from Huber density

Huber density, connected to Huber loss, can be defined as: $$ f(x) = \frac{1-\epsilon}{\sqrt{2\pi}} e^{-\rho_k(x)} $$ where $$ \rho_k(x) = \begin{cases} \frac{1}{2} x^2 & |x|\le k \\...
3
votes
0answers
35 views

Is M-estimation valid only for regression models?

Is M-estimation valid only for regression models or does it's working hold good for robust estimation of parameters in other statistical models? I understand that M-estimators are asymptotically ...
7
votes
2answers
126 views

Robustness of correlation test to non-normality

I'm trying to reconcile two seemingly opposite statements about robustness to non-normality of the Pearson's correlation test statistic (where the null means "no correlation"). This CV answer says: ...
0
votes
0answers
25 views

Two more questions about Count Data

I almost became an opponent of the transformation of count data. (quite famous reference). But I still did not find answers to some important questions about non-transformed count data (I consider ...
0
votes
0answers
19 views

Robustness criterion (1978)

I am referring to bradley's journal relating to the robustness criterion. According to his liberal criterion, the simulated type 1 error rate have to between 0.5 alpha < p < 1.5 alpha. since i ...
0
votes
1answer
323 views

p values and significance in RLM (MASS package) R

I'm running some regression analyses and got pretty confused about R's output when it comes to robust regression models. When I run a OLS -- using the command ...
1
vote
0answers
31 views

Influence function of OLS to check the robustness

I understand what is the influence function. I'm just blocked on this exercise. We consider the regression following. $y_i = x_i^T \beta + \epsilon_i$ Can somebody help me to calculate the influence ...
1
vote
0answers
20 views

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 ...
0
votes
0answers
21 views

Robust Estimation of Negative Binomial: Help in understanding the code

I would like to understand following R code. Robust methods used here works, I understood it, but sometimes too many options for choice is not a good thing - now I am confused which method should I ...
2
votes
0answers
25 views

Robust Bootstrap Covariance Estimator

I sometimes see particular bootstrap repetitions give "wild" regression coefficient estimates for one or more bootstrap resamples. This occurs more often in binary logistic regression. One or two ...
0
votes
0answers
52 views

Questions about negative binomial distribution

1) I know that my data is NB distributed. But also I know that it has several outliers (probably, zeros and extremely big numbers). How can I estimate NB? I found the trick answered here, on CV site, ...
1
vote
0answers
28 views

Inter-quartile range

If we have a non-normal distribution say (Weibull) and we used a robust estimator of standard deviation in term of inter-quartile range (IQR). Is it possible we replace IQR=3*SD?
0
votes
0answers
36 views

$r^2$ using Robust Regression

I am interested in measure of quality of robust regression. How should I calculate it? It is clear that $r^2$ of robust regression should be smaller than $r^2$ OLS regression. Even if OLS is totally ...
0
votes
0answers
8 views

how does the Qn estimator work

I was given a short introduction to alternative robust methods to the MAD used in statistics, one of them being the Qn estimator. This one is denoted as: \begin{equation} Q_n = d( |(x_i - x_j)|:i<j ...
3
votes
0answers
43 views

alternative to IQR

I am currently making a short literature study on robust and efficient estimators. Some very well known are the median absolute deviation (MAD) and the interquartile range(IQR). However they both have ...
1
vote
0answers
31 views

Is there an R function for calculating a scale M-estimator based on Tukey's bisquare rho function? [closed]

The bisquare function is the second example in this wikipedia page https://en.wikipedia.org/wiki/Redescending_M-estimator#Examples
1
vote
0answers
34 views

Interpretation of drop.test output in R

Could someone clarify the purpose of the drop in dispersion test (drop.test) in the Rfit package in R? I think that it shows the difference in explanatory power ...
0
votes
0answers
15 views

Deleting outliers in (almost) uniform graph

I wrote an algorithm that finds the centroids of rectangles which are on the same line and groups these rectangles together. I know that only those rectangles with a roughly equal size belong together,...
3
votes
0answers
29 views

Rules of thumb for a proportion of outliers depending on the dimension

I am implementing and benchmarking different "robust" PCA (principal component analysis, see for instance Robust Principal Component Analysis?) for data that should align (I have no prior on the ...
1
vote
1answer
27 views

When to use robust methods and how to report them in a paper?

Wilcox in his package WRS in R software managed to provide easily accessible robust techniques to conduct usual hypothesis testing like t-test, ANOVA etc. I've ...
1
vote
0answers
27 views

robust estimator when $\mu/\sigma$ is constant

I have a large set of measurements consisting each of about 100 points, (normally distributed), with up to 20% outliers. The outliers are all shifted towards positive values. From the physics, I know ...
1
vote
1answer
64 views

Robust correlation in python? [closed]

Is some kind of robust (especially to outliers) correlation methods easily available in python? I have a suspicion that outliers might be causing false positives in my data.
0
votes
0answers
51 views
1
vote
1answer
88 views

How to choose t-distribution degrees of freedom in “robust” Bayesian linear models

It is well known that in both frequentist and Bayesian linear models, outliers can greatly influence the parameter estimates. Consider the simple example where one outcome variable, $y$, is predicted ...
1
vote
0answers
88 views

confidence intervals for cox proportional hazards with 0 events in one treatment arm

We are performing a multivariate cox proportional hazards analysis with 6 covariates. Two of the covariates (Pre_ASCT_response2 and NGS_graft_MRD2) are binary covariates with 0 events in one condition....
1
vote
0answers
27 views

Inflated Type 1 error in glmer (for main effect but not interaction?)

I am doing simulations of type 1 error, power, and power' (power corrected for anti conservativity) for research on a specific application of (g)lmer, namely to small-N designs of longitudinal ...
0
votes
0answers
40 views

Should I check control variables for robustness?

I am working with count data and using a negative binomial regression. I have 3 core independent variables and control variables (age,education and sex). But the problem is that I have 8 age groups ...
0
votes
0answers
17 views

how can I calculate type one error rate?

how can I calculate type one error rate ? my study on robust estimators I search for calculating type one error to compare I found several article for that but no one explain how
0
votes
0answers
52 views

Fixed effects robust X random effects

I'm new to econometrics and Stata and I need some help. I'm trying to estimate a panel data model and I'm would like how to compare a robust fixed effects and a random effects model in Stata. I'm ...
0
votes
0answers
19 views

Correlation robust to outliers

I am concerned with Pearson correlation. My idea is to throw away the outliers in terms of absolute value, as it seems appropriate to my case. However, I don't know if I should do it only according to ...
0
votes
1answer
71 views

Correlation values aren't significant but the coefficients of regression are

I ran a regression model between X and y. I used robust regression. The results are significant, but when I ran a Pearson correlation I found that the correlation values aren't significant. I don't ...
1
vote
0answers
55 views

How to check the number of outliers in my data?

I have multivariate data and want to check for outliers. I guess there are both outliers in the predictor variables and the dependent variable. My idea was to fit robust regression methods after ...
0
votes
0answers
91 views

Robust regression and singular error

I got this error message from a robust regression in r ; "'x' is singular: singular fits are not implemented in 'rlm'" I could solve it by removing some variables. My question is , can i progrom r ...