Recently in Gao and Stoev (2018) it was established that the concentration of maxima phenomenon is the key to solving the exact sparse support recovery problem in high dimensions. This phenomenon, known also as relative stability, has been little studied in the context of dependence. Here, we obtain bounds on the rate of concentration of maxima in Gaussian triangular arrays. These results are used to establish sufficient conditions for the uniform relative stability of functions of Gaussian arrays, leading to new models that exhibit phase transitions in the exact support recovery problem. Finally, the optimal rate of concentration for Gaussian arrays is studied under general assumptions implied by the classic condition of Berman (1964).
The spectral density function describes the second-order properties of a stationary stochastic process on R d . This paper considers the nonparametric estimation of the spectral density of a continuous-time stochastic process taking values in a separable Hilbert space. Our estimator is based on kernel smoothing and can be applied to a wide variety of spatial sampling schemes including those in which data are observed at irregular spatial locations. Thus, it finds immediate applications in Spatial Statistics, where irregularly sampled data naturally arise. The rates for the bias and variance of the estimator are obtained under general conditions in a mixed-domain asymptotic setting. When the data are observed on a regular grid, the optimal rate of the estimator matches the minimax rate for the class of covariance functions that decay according to a power law. The asymptotic normality of the spectral density estimator is also established under general conditions for Gaussian Hilbertspace valued processes. Finally, with a view towards practical applications the asymptotic results are specialized to the case of discretely-sampled functional data in a reproducing kernel Hilbert space.
Network operators and system administrators are increasingly overwhelmed with incessant cyber-security threats ranging from malicious network reconnaissance to attacks such as distributed denial of service and data breaches. A large number of these attacks could be prevented if the network operators were better equipped with threat intelligence information that would allow them to block or throttle nefarious scanning activities. Network telescopes or "darknets" offer a unique window into observing Internet-wide scanners and other malicious entities, and they could offer early warning signals to operators that would be critical for infrastructure protection and/or attack mitigation. A network telescope consists of unused or "dark" IP spaces that serve no users, and solely passively observes any Internet traffic destined to the "telescope sensor" in an attempt to record ubiquitous network scanners, malware that forage for vulnerable devices, and other dubious activities. Hence, monitoring network telescopes for timely detection of coordinated and heavy scanning activities is an important, albeit challenging, task. The challenges mainly arise due to the non-stationarity and the dynamic nature of Internet traffic and, more importantly, the fact that one needs to monitor high-dimensional signals (e.g., all TCP/UDP ports) to search for "sparse" anomalies. We propose statistical methods to address both challenges in an efficient and "online" manner; our work is validated both with synthetic data as well as real-world data from a large network telescope.
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