On the empirical distribution of eigenvalues of a class of large dimensional random matrices
Journal of Multivariate Analysis
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications
Computational Statistics & Data Analysis
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Mode estimation is an important task, because it has applications to data from a wide variety of sources. Many mode estimates have been proposed with most based on nonparametric density estimates. However, mode estimates obtained by such methods, although they perform excellently with large sample sizes, perform non-satisfactorily with practical (i.e., small to moderate) sample sizes. Recently, Bickel (2003) proposed an efficient method to estimate the mode of continuous univariate data, and showed that its performance is excellent with small to moderate sample sizes. In this paper, we extend Bickel's method to continuous multivariate data by using the multivariate Box-Cox transform. The excellent performance of the proposed method at practical sample sizes is demonstrated by simulation examples and two real examples from the fields of climatology and image recognition.