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In this paper we provide a provably convergent algorithm for the multivariate Gaussian Maximum Likelihood version of the Behrens– Fisher Problem. Our work builds upon a formulation of the log-likelihood function proposed by Buot and Richards [5]. Instead of focusing on the first order optimality conditions, the algorithm aims directly for the maximization(More)
Operator self-similarity naturally extends the concepts of univariate self-similarity and scale invariance to multivariate data. Beyond a vector of Hurst parameters, operator self-similarity models also involve a mixing matrix. The present contribution aims at estimating the collection of Hurst parameters in the case where the mixing matrix is not diagonal.(More)
1 Introduction The concept of citizens as sensors is becoming broadly utilised as collection-enabling technologies are widely adopted in consumer devices. As a consequence, the term crowdsourcing is generic, and describes an array of different activities carried out by people in an active (e.g. filling out a survey) or passive (e.g. information mined from(More)
Internet traffic monitoring is a crucial task for network security. Self-similarity, a key property for a relevant description of internet traffic statistics, has already been massively and successfully involved in anomaly detection. Self-similar analysis was however so far applied either to byte or Packet count time series independently, while both signals(More)
In this paper, the fault detection problem for nonlinear dynamic power systems based an observer is treated. The nonlinear dynamic model based on differential algebraic equations (DAE) is transformed in to ordinary differential equations (ODE). Three nonlinear observers are used and compared for generating the residual signals. Which are: the extended(More)
Scaling phenomena, or self-similarity, are pervasive in nature and in data, and have been the subject of decades of research in probability and statistics. In higher dimension, self-similarity presents new challenges. These include the theoretical consequences of matrix-scaling, anisotropy, non-identifiability, and their impact on inferential pursuits. In(More)