Is there an academic article or book that I can refer to when using these guidelines in my thesis? Respected Karen! Can you please add or send me the reference of this justification. Advance thanks. In plot number 2, I do not understand why you want to drop the outlier?? To my point of view, it tells you that the model is rather robust.
Remind that a statistical model should only been apply for prediction within the data range used for its calibration. The larger the data range, the more robust it will be for predicting in new situations. When cleaning a large dataset for outliers, does a separate outlier analysis have to be run for every single regression analysis one plans on running? For instance, does running 30 different regressions typically require 30 separate outlier analyses?
If so, do the outliers need to be added back into the data set before running the next outlier analysis? If multiple outlier analyses are not required in this case, is just one outlier analysis enough i. After checking all of the above, I do not understand the rationale for keeping an outlier that affects both assumptions and conclusion just by principle.
In a survival analysis, maybe somebody died of a car accident but you dont have the death certificate. Biomarkers cant predict that, neither can most genes. It is not really the outlier there is anything wrong with, but the inability of most parametric tests to deal with 1 or 2 extreme observations. If robust estimators are not available, downweighting or dropping a case that changes the entire conclusion of the model seems perfectly fair and reporting it.
In example two, the outlier should have little effect on the slope estimate but it ought to have a BIG effect on the standard error of the slope estimate. It would definitely be worth investigating how it came about.
A lot might depend on the physical situation involved, whether we are dealing with correlation or with truly independent and dependent variables, etc. Can we remove outliers based on CV.
To lower down CV, change the replication data value but without any change the mean value of treatment. I tried this in some study and the effects are not trivial. First, my data had some observations which clearly were quite far from the mean sd of over I included them and my parameters were significant all through.
I am analysing household consumption expenditure and conclusions based on outliers will most probably be unrepresentative. I tried the robust errors suggested here as well. I think with outliers their effect is inflating the variances and hence parameter significance robust errors should be enough, as much as we trust the underlying framework. What happens if you take out the outlier, and things become more significant? What would you do in this situation? It passes above most of the data at the low doses because of the high outlier at the bottom dose.
For comparison, removing the outlier entirely and fitting the remaining data gives the green line. There is clearly quite a large difference between the fits — in particular, the red line has a smaller slope than the green line. In a bioassay, this could mean that the assay fails system suitability criteria, or if it passes, the relative potency estimate could be inaccurate. Given the problems outliers cause, the simplest solution appears to be just remove them. However, this is not as simple as it sounds, as it is not always obvious from the data what an outlier is and what it is not.
In the figures above, the point at the lowest dose with a very high response around 7 is clearly an outlier. If the response was much lower e. But in-between there are a range of values around 4 or 5 where it might not be as clear whether this is an outlier or just a slightly high response. Making a manual judgement in such cases would be difficult, and manual judgements are subjective in any case — different people might make different decisions on whether a particular point is an outlier or not.
For consistency and for regulatory reasons it is better to use an automatic method of detecting outliers. Both are commonly available in software packages used for bioassay analysis, including our commercially available software, QuBAS. The adjustment step is a bit different in the two methods, which can lead to different points being identified as residuals in each case. A completely different method of removing outliers is to try and transform the data.
Sometimes a response that looks like an outlier can actually be due to increased variation in one part of the dose-response curve. For example, in the figure below, the responses at the lowest dose are very far apart so it looks like one of them is an outlier although there is no way of telling which one.
Now the bottom dose group does not look unusual — the responses are no further apart than at several of the other doses. Therefore, there are actually no outliers here at all!
Michael R. Chernick Ptdstudent Ptdstudent 71 1 1 silver badge 2 2 bronze badges. Do you have reason to believe that they are "bad" data, i.
In general, 0 is a reasonable number of outliers to remove. Without further information demonstrating that an "outlier" is mistaken or irrelevant, 0 is the only defensible number of outliers to remove. However, it's possible and usually a good idea to conduct analyses both with and without the outliers to assess how much the outliers influence the results. Add a comment. Active Oldest Votes. Improve this answer. Ben Ben Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.
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