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Register a new userA different approach for choosing a threshold in peaks over threshold
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Added by
Lizanne Raubenheimer
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
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Abstract In Extreme Value methodology the choice of threshold plays an important role in efficient modelling of observations exceeding the threshold. The threshold must be chosen high enough to ensure an unbiased extreme value index but choosing the threshold too high results in uncontrolled variances. This paper investigates a generalized model that can assist in the choice of optimal threshold values in the gamma positive domain. A Bayesian approach is considered by deriving a posterior distribution for the unknown generalized parameter. Using the properties of the posterior distribution allows for a method to choose an optimal threshold without visual inspection.
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We investigate a Poisson sampling design in the presence of unknown selection probabilities when applied to a population of unknown size for multiple sampling occasions. The fixed-population model is adopted and extended upon for inference. The complete minimal sufficient statistic is derived for the sampling model parameters and fixed-population parameter vector. The Rao-Blackwell version of population quantity estimators is detailed. An application is applied to an emprical population. The extended inferential framework is found to have much potential and utility for empirical studies.
Bitmap indexes are routinely used to speed up simple aggregate queries in databases. Set operations such as intersections, unions and complements can be represented as logical operations (AND, OR, NOT). However, less is known about the application of bitmap indexes to more advanced queries. We want to extend the applicability of bitmap indexes. As a starting point, we consider symmetric Boolean queries (e.g., threshold functions). For example, we might consider stores as sets of products, and ask for products that are on sale in 2 to 10 stores. Such symmetric Boolean queries generalize intersection, union, and T-occurrence queries. It may not be immediately obvious to an engineer how to use bitmap indexes for symmetric Boolean queries. Yet, maybe surprisingly, we find that the best of our bitmap-based algorithms are competitive with the state-of-the-art algorithms for important special cases (e.g., MergeOpt, MergeSkip, DivideSkip, ScanCount). Moreover, unlike the competing algorithms, the result of our computation is again a bitmap which can be further processed within a bitmap index. We review algorithmic design issues such as the aggregation of many compressed bitmaps. We conclude with a discussion on other advanced queries that bitmap indexes might be able to support efficiently.
QCD exhibits complex dynamics near S-wave two-body thresholds. For light mesons, we see this in the failure of quark models to explain the $f_0(500)$ and $K_0^*(700)$ masses. For charmonium, an unexpected $X(3872)$ state appears at the open charm threshold. In heavy-light systems, analogous threshold effects appear for the lowest $J^P = 0^+$ and $1^+$ states in the $D_s$ and $B_s$ systems. Here we describe how lattice QCD can be used to understand these threshold dynamics by smoothly varying the strange-quark mass when studying the heavy-light systems. Small perturbations around the physical strange quark mass are used so to always remain near the physical QCD dynamics. This calculation is a straightforward extension of those already in the literature and can be undertaken by multiple lattice QCD collaborations with minimal computational cost.
For more than a century, fingerprints have been used with considerable success to identify criminals or verify the identity of individuals. The categorical conclusion scheme used by fingerprint examiners, and more generally the inference process followed by forensic scientists, have been heavily criticised in the scientific and legal literature. Instead, scholars have proposed to characterise the weight of forensic evidence using the Bayes factor as the key element of the inference process. In forensic science, quantifying the magnitude of support is equally as important as determining which model is supported. Unfortunately, the complexity of fingerprint patterns render likelihood-based inference impossible. In this paper, we use an Approximate Bayesian Computation model selection algorithm to quantify the weight of fingerprint evidence. We supplement the ABC algorithm using a Receiver Operating Characteristic curve to mitigate the effect of the curse of dimensionality. Our modified algorithm is computationally efficient and makes it easier to monitor convergence as the number of simulations increase. We use our method to quantify the weight of fingerprint evidence in forensic science, but we note that it can be applied to any other forensic pattern evidence.
The time over threshold is a widely used quantity to describe signals from various detectors in particle physics. Its electronics implementation is straightforward and in this paper we present the studies of its behavior in the presence of noise. A unique comb-like structure was identified in the data for a first time and was explained and modeled successfully. The effects of that structure on the efficiency and resolution are also discussed.
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