The main aim of data mining is to extract knowledge from, usually very large, datasets. Data mining techniques are often applied to gain initial insights about the data and complement statistical models. This special issue focuses on the interface between data mining and statistical modelling, with special emphasis on robust statistics. Very large datasets, especially those that are machine gen...

For group-randomized trials, randomization inference based on rank statistics provides robust, exact inference against nonnormal distributions. However, in a matched-pair design, the currently available rank-based statistics lose significant power compared to normal linear mixed model (LMM) test statistics when the LMM is true. In this article, we investigate and develop an optimal test statist...

A large volume of existing research attempts to understand who uses Tor and how the network is used (and misused). However, conducting measurements on the live Tor network, if done improperly, can endanger the security and anonymity of the millions of users who depend on the network to enhance their online privacy. Indeed, several existing measurement studies of Tor have been heavily criticized...

The generic problem of estimation and inference given a sequence of i.i.d. samples has been extensively studied in the statistics, property testing, and learning communities. A natural quantity of interest is the sample complexity of the particular learning or estimation problem being considered. While sample complexity is an important component of the computational efficiency of the task, it i...

A second order statistics spectrum estimation (SOSSE) method for speech enhancement is presented. DFT amplitude spectral components of noisy signal are assumed to be random values. Upon first and second order statistic values estimation of noise-only spectrum, an enhancement of noisy signal spectrum was performed. As a reference, a fast discrete cosine transform based signal subspace (FDCTSS) m...

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In the typical setting investigated till now, each classifier is trained on data taken or resampled from a common data set, or randomly selected partitions thereof, and thus experiences similar quality of training data. However, in...

A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the history of one time series to predict the current state of another, conditional on all other past information. While the Granger Causality (GC) principle remains uncontested, its literal application is challenged by practica...

Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform...

Article history: Received 27 September 2007 Received in revised form 18 July 2008 Accepted 12 January 2009

This paper presents a robust method for segmenting images based on the motion of different scene objects. The presented algorithm combines a robust estimator named Selective Statistical Estimator (SSE) with a median filtering technique to enforce the spatial continuity into a robust estimator. The algorithm is tested on a number of real image sequences contain multiple moving objects. The propo...