相关论文: When Outliers Are Different

The Gamma Ray Burst GRB920229 has four extreme and unprecedented properties; a rise in brightness with an e-folding time scale of $220 \pm 30 \mu s$, a fall in brightness with an e-folding time scale of $400 \pm 100 \mu s$, a large change…

We propose that catastrophic events are "outliers" with statistically different properties than the rest of the population and result from mechanisms involving amplifying critical cascades. Applications and the potential for prediction are…

The observational diversity of ``gamma-ray bursts'' (GRBs) has been increasing, and the natural inclination is a proliferation of models. We explore the possibility that at least part of this diversity is a consequence of a single basic…

The aim of the paper is to show that the presence of one possible type of outliers is not connected to that of heavy tails of the distribution. In contrary, typical situation for outliers appearance is the case of compact supported…

This study deals with the problem of outliers in ordinal response model, which is a regression on ordered categorical data as the response variable. ``Outlier" means that the combination of ordered categorical data and its covariates is…

In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictable behaviour. We need to know whether any given input belongs…

A new procedure for smoothing a gamma-ray burst (GRB) lightcurve and calculating its variability is presented. Applying the procedure to a sample of 25 long GRBs, we have obtained a very tight correlation between the variability and the…

The 'event' that triggers a gamma ray burst cannot last for more than a few seconds. This is, however, long compared with the dynamical timescale of a compact stellar-mass object ($\sim 10^{-3}$ seconds). Energy is assumed to be released as…

Out-of-distribution (OOD) detection is an important task in machine learning systems for ensuring their reliability and safety. Deep probabilistic generative models facilitate OOD detection by estimating the likelihood of a data sample.…

Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into the point, contextual and collective outliers.…

The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a different population, or cellwise outliers, which are deviating…

We present Monte-Carlo simulations of a cosmological population of repeating fast radio burst (FRB) sources whose comoving density follows the cosmic star formation rate history. We assume a power-law model for the intrinsic energy…

While Gamma-Ray Burst (GRBs) are clear and distinct observed events, every individual GRB is unique. In fact, GRBs are known for their variable behaviour, and BATSE was already able to discover two categories of GRB from the T90…

The detection of gravitational waves with Pulsar Timing Arrays (PTAs) requires precise measurement of the difference between the pulsars' timing models and their observed pulses, as well as dealing with numerous and sometimes hard to…

Gamma-ray bursts (GRBs) have been phenomenologically classified into long and short populations based on the observed bimodal distribution of duration. Multi-wavelength and multi-messenger observations in recent years have revealed that in…

Gamma Ray Burst (GRBs) are empirically classified as long-duration GRBs (LGRBs, $>$ 2s) and short-duration GRBs (SGRBs, $<$ 2s). Physically they can be grouped into two distinct progenitor categories: those originating from collapse of…

In order to allow machine learning algorithms to extract knowledge from raw data, these data must first be cleaned, transformed, and put into machine-appropriate form. These often very time-consuming phase is referred to as preprocessing.…

Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to…

This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In presence of outliers, the standard Fay-Herriot model,…

This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belong to the same distribution as a reference data set or are…