I don't know of any. You must have the data!
Hi,
Is there any way to find outlier without having the distribution of data?
Thanks and Regards
I don't know of any. You must have the data!
For example any nonparametric test or other method or algorithm?
If you don't have any data it is impossible to do any statistical analysis. You can only do theoretical speculation.
All outlier or anomaly detection is with respect to some model, whether you're looking at a univariate case of observations that deviate from the variable mean (e.g., 2 SD from the mean could be considered a "large" deviation) or looking at those observations that deviate greatly from a fitted trend line. Therefore, it isn't about the distribution to which you think it belongs. It's about what model you want to accept as representing your data and therefore defines what it means to be significantly different from that model. Suppose you make a bad choice of model (choosing means on a non-symmetric distribution of data, for instance). Then you may say a bunch of observations are extreme, to which on that assumption is true, but it is only because that model of the data was a bad choice. The same applies to trying to fit a straight line to a curvilinear relationship. You might classify observations as being extreme, which is true on that assumption, but it's the model choice that was poor. Models depend entirely on what sort of problem you're dealing with, though. How are you trying to model your data? Is it a univariate observation? A multidimensional model? A linear regression? An association rule? In statistical models you'll find literature on outlier detection. In machine learning literature, you'll usually find anomaly detection. They're both dealing with the same fundamental thing, and there are many ways to calculate the deviation from a model, but that is ultimately all you're doing.
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