The UU-test for statistical modeling of unimodal data. (February 2022) - Chasani, Paraskevi; Likas, Aristidis
Highlights: We propose UU-test that is a new method for deciding on dataset unimodality. The method attempts to approximate the empirical cdf of the dataset using a cdf that is piecewise linear, unimodal and models the data sufficiently. A unique feature is that the method also provides a statistical model of a unimodal dataset in the form of a uniform mixture model. The method can be employed in various data analysis tasks (e.g. clustering) where deciding on dataset unimodality is important. Abstract: Deciding on the unimodality of a dataset is an important problem in data analysis and statistical modeling. It allows to obtain knowledge about the structure of the dataset, i.e. whether data points have been generated by a probability distribution with a single or more than one peaks. Such knowledge is very useful for several data analysis problems, such as for deciding on the number of clusters and determining unimodal projections. We propose a technique called UU-test (Unimodal Uniform test) to decide on the unimodality of a one-dimensional dataset. The method operates on the empirical cumulative density function (ecdf) of the dataset. It attempts to build a piecewise linear approximation of the ecdf that is unimodal and models the data sufficiently in the sense that the data corresponding to each linear segment follows the uniform distribution. A unique feature of this approach is that in the case of unimodality, it also provides a statistical model of the data in the form of a Uniform Mixture Model. We present experimental results in order to assess the ability of the method to decide on unimodality and perform comparisons with the well-known dip-test approach. In addition, in the case of unimodal datasets we evaluate the Uniform Mixture Models provided by the proposed method using the test set log-likelihood and the two-sample Kolmogorov-Smirnov (KS) test.