Robust regression and outlier detection
Robust regression and outlier detection
Robust estimators of the mode and skewness of continuous data
Computational Statistics & Data Analysis
Evaluation of robust estimators applied to fluorescence assays
EURASIP Journal on Advances in Signal Processing
Short communication: Efficient estimation of the mode of continuous multivariate data
Computational Statistics & Data Analysis
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Advances in computing power enable more widespread use of the mode, which is a natural measure of central tendency since it is not influenced by the tails in the distribution. The properties of the half-sample mode, which is a simple and fast estimator of the mode of a continuous distribution, are studied. The half-sample mode is less sensitive to outliers than most other estimators of location, including many other low-bias estimators of the mode. Its breakdown point is one half, equal to that of the median. However, because of its finite rejection point, the half-sample mode is much less sensitive to outliers that are all either greater or less than the other values of the sample. This is confirmed by applying the mode estimator and the median to samples drawn from normal, lognormal, and Pareto distributions contaminated by outliers. It is also shown that the half-sample mode, in combination with a robust scale estimator, is a highly robust starting point for iterative robust location estimators such as Huber's M-estimator. The half-sample mode can easily be generalized to modal intervals containing more or less than half of the sample. An application of such an estimator to the finding of collision points in high-energy proton-proton interactions is presented.