(Wiley Series in Probability and Mathematical Statistics). Realize that when you winsorize you are obtaining statistics that have less variance than the true data. I feel more comfortable with using the 0.01 quantile (1 in each tail) than using the 0.05 quantile (5 in each tail). Robust Statistics: The Approach Based on Influence Functions If you must Winsorize, exclude the smallest percentile that eliminates the problematic outliers. Hampel, Frank, Elvezio Ronchetti, Peter Rousseeuw, and Werner Stahel.ġ986. In providing a resistant fit, mmregress also identifies outliers and high leverage points. For regression, the robust regression package mmregress by Verardi and Croux is superior ( findit). If you choose to winsorize your data I suggest you check out the command winsor2. Note, winsorizing and deleting observations can introduce statistical bias. You should be reducing the influence of very large residuals, not the original values. We can keep them as they are, winsorize the observations (change their values), or delete them. You could do the last part a dozen different ways, but hopefully that is clear enough to realize what is going on when you winsorize a. If you winsorize a variable that is destined to be the response in a regression, you probably be altering the wrong observations. Then lets make a new variable named winsorX replacing all values below the 5th and 95th percentile with the associated percentile. This video is an alternative to video 7.Some details.
We use the Stata program pscore.ado (Becker and Ichino. On my channel you find SPSS & Stata tutorial E-Learning videos Subscribe and like my videos to support my channel.
Stata winsorize how to#
In the literature on robustness, you will commonly see 10% or even 20% winsorized (or trimmed) means.Īlso, winsorizing and trimming can be bettered by other methods which adapt to likely outliers, and which do not require much of an advance guess about how many there are. This video shows how to winsorize data with Stata if there are potential outliers present in your data. the latter three indicators, we winsorize all observations at the first percentile in each tail. To that end, I disagree with the default levels of 1% winsorization in winsor2. As a change from my usual posts, I thought I’d note five small things I’ve learned recently, mostly to do with Stata, with the hope that they might help others, or at least jog my memory when I. Studies of high quality data generally show percentages of gross errors higher than 1% in each tail, sometimes much higher. You will probably miss most outliers if you winsorize 1% in each tail.
0 kommentar(er)
