robust

Hello. There are several robust regression methods like LAR-(aka LAV-, LAD-, L1-Norm-)Regression, Quantil-Regression, M-Estimator, ... They are assumed to be especially appropriate for data, that does not fulfill the 5 OLS conditions. The major part of the robust regression literature (I read)...

I am investigating the effects of age and exercise in protein expression in mouse motor neurons. I have four groups each with a limited number of measurements associated with it. Group (number of mice) Young (6) Young exercised (3) Old...

I'm attempting to weight a mean to make outliers less impactful. I know people use a median here often but am looking to try to make a mean that's rbust to outliers. That is the further an observation is from the mean the less weight it has. I'm sure this has been done before. In...

What criteria should I used to set "#" in the bw(#) option using ivreg2 with the options gmm2s and robust also specified? I set it high for the efficiency benefits associated with including more information in the weighting matrix, but I don't know if there's a cost to increasing #, and my...

I’ve spent considerable time researching (on this site and elsewhere) for an appropriate statistical descriptor for a non-parametric (non-normally distributed) data set. I think I have the answer but would love any feedback as this is not an area of strength for me. TASK / DATA DESCRIPTION...

I can not find any paper about, robust statistics where they are looking to outliers not like a outlierts but like that model miss specification. exp. i mean that i have sample from N(0,1) ,50 observation and there are value 5 and -4 this 2 are outliers, but TRUE distribution is Caushy...