What are the best methods for fitting the 'mode' of data sampled from a continuous distribution?
Since the mode is technically undefined (right?) for a continuous distribution, I'm really asking 'how do you find the most common value'?
If you assume the parent distribution is gaussian, you could bin the data and find say the mode is the bin location with the greatest counts. However, how do you determine the bin size? Are there robust implementations available? (i.e., robust to outliers). I use
numpy, but I can probably translate
R without too much difficulty.