Rare Events Analysis Research Articles

Is there an association between 30-day mortality and adrenaline infusion rates in post-ROSC patients? A retrospective observational analysis.

Revised guidelines for the management of cardiac arrest have placed greater emphasis on early defibrillation and closed chest compressions; subsequently there has been a significant rise in the number of patients gaining a return of spontaneous circulation (ROSC). As a consequence, emergency medical services have realised the importance of therapies delivered during this phase of care. In some Trusts this includes the use of inotropic agents to augment the cardiovascular system and maintain adequate cerebral and coronary perfusion pressures to mitigate the effects of post-cardiac arrest syndrome. Currently, limited evidence exists with regards to the efficacy of such treatments in the pre-hospital phase. Retrospective observational analysis of out-of-hospital cardiac arrest patients who received an adrenaline infusion by critical care paramedics. Infusion rates, time of call (ToC) to ROSC and 30-day mortality were compared. Over a 2-year period, 202 patients were recorded as having an adrenaline infusion commenced. Of these, 25 were excluded as they did not meet criteria or had incomplete data and 22 were excluded as the infusion was stopped at scene; 155 patients were admitted to hospital. There were no survivors in the non-shockable group and three survivors in the shockable group at 30 days. A rare events analysis found no relationship between infusion rate, ToC to ROSC and 30-day mortality (Wald chi2, 1.37). Commencement of adrenaline infusions in post-ROSC was associated with significant 30-day mortality, especially in non-shockable rhythms. Further research is needed to elucidate whether this intervention has any benefit in the post-ROSC patient.

Использование емкостного метода для анализа исторических событий

The use of the capacity method of rare events analysis [1,2] for the historical events analysis is demonstrated using the example of the Russian-Turkish wars. Modeling rare events as events of overflow of a certain capacity, for example, a cup of patience, it turns out to recover from these rare events the resulting function of the difference between incoming and outgoing disturbance flows. For different variants of representing the effect of an event, different functions were obtained, which were given an interesting interpretation, as example the speed of preparation for the next war.

Capacity Method of Rare Events Analysis in the Area of Services

Imagine that you are owner of some service. You need to determine for a certain future period the work plan of your craftsmen, the number of consumables needed. To do this, you need to make a forecast of future services number. Classical mathematical methods of working with time series are not suitable for this task. Aggregation of data on services by months and the compilation of a time series can only confuse. Forecasting services should be performed using methods designed to work with rare events. Rare events are devoted to relatively few works. Methods for the study of rare events are significantly less than methods for analyzing frequent events (time series). The most popular method of studying rare events at the moment is the use of the theory of random processes, which uses a stream of Poisson or Erlang events. However, using random streams, one cannot predict the very moment of the occurrence of an event. The paper describes an approach to the rare events analysis, which is based on: dividing events by identifiers of the sources in which they are formed; regression process parameters occurring within the sources, resulting in these events formation; search by any known method of parameters change patterns; the process start itself to obtain a forecast of the following events time occurrence. For the consumption processes and the disturbances growth process, which are the most common processes of the events formation in the economy, a method is proposed for restoring the consumption or accumulating disturbances rate from the rare events history. Services as can be modeled as the process of accumulating disturbances to a certain level. The article is devoted to the application of the capacity method of rare events analysis on real data in the service sector (haircut in a hairdresser, a manicure in a beauty salon, cellular communication services). The task is to restore the function that leads to the acquisition of services, and then predict the following events.

ЕМКОСТНЫЙ МЕТОД АНАЛИЗА РЕДКИХ СОБЫТИЙ, ОЦЕНКА ПОГРЕШНОСТИ ВСЛЕДСТВИЕ КОНКУРЕНЦИИ ИЛИ ПОТЕРИ ДАННЫХ

Цель: в статье кратко изложена идея емкостного метода анализа редких событий. Основное внимание уделено вопросу падения точности в результате потери части данных. Обсуждение: оценка погрешности проводилась с помощью компьютерного моделирования. Описана схема проведения экспериментов. Помимо этого, производилась оценка погрешности классическим методом, когда редкие события представлены временными рядами. В качестве оценки погрешности использовалась средняя относительная погрешность и средняя квадратичная погрешность. Результаты: исследование показало, что емкостный метод анализа редких событий обладает лучшей точностью по сравнению с классическим методом, независимо от того, какая часть данных теряется, 10% 30% или 50%. Однако в случае частых событий погрешность классического метода сравнивается. Делается вывод, что для анализа редких событий необходимо переходить на емкостный метод.

Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis

© 2019 Elsevier Ltd Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples.

Using Fiscal Ratios to Predict Local Fiscal Distress

Municipal fiscal distress experienced by cities such as Detroit, Michigan; San Bernardino,California; and Harrisburg, Pennsylvania, has generated an impressive body of work by researchers seeking to understand underlying factors in an effort to prevent future distress. Recent research has been informative and appears to be coalescing around a handful of predictive factors such as reserves and debt; however, the generalizability of the findings is limited because the research has focused on case studies or within-state analyses. This study draws from a large national sample of local governments in the United States over a 10-year period and incorporates robust methodology for rare events analysis. Consistent with previous research on local fiscal distress, we find evidence that unreserved general fund balances, unrestricted net assets, long-term obligations, and local unemployment are statistically associated with municipal defaults and bankruptcies.

Empirical Data Analytics

In this paper, we propose an approach to data analysis, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space. At the core of the proposed new approach is the typicality—an empirically derived quantity that resembles probability. This nonparametric measure is a normalized form of the square centrality (centrality is a measure of closeness used in graph theory). It is also closely linked to the cumulative proximity and eccentricity (a measure of the tail of the distributions that is very useful for anomaly detection and analysis of extreme values). In this paper, we introduce and study two types of typicality, namely its local and global versions. The local typicality resembles the well-known probability density function (pdf), probability mass function, and fuzzy set membership but differs from all of them. The global typicality, on the other hand, resembles well-known histograms but also differs from them. A distinctive feature of the proposed new approach, empirical data analysis (EDA), is that it is not limited by restrictive impractical prior assumptions about the data generation model as the traditional probability theory and statistical learning approaches are. Moreover, it does not require an explicit and binary assumption of either randomness or determinism of the empirically observed data, their independence, or even their number (it can be as low as a couple of data samples). The typicality is considered as a fundamental quantity in the pattern analysis, which is derived directly from data and is stated in a discrete form in contrast to the traditional approach where a continuous pdf is assumed a priori and estimated from data afterward. The typicality introduced in this paper is free from the paradoxes of the pdf. Typicality is objectivist while the fuzzy sets and the belief-based branch of the probability theory are subjectivist. The local typicality is expressed in a closed analytical form and can be calculated recursively, thus, computationally very efficiently. The other nonparametric ensemble properties of the data introduced and studied in this paper, namely, the square centrality, cumulative proximity, and eccentricity, can also be updated recursively for various types of distance metrics. Finally, a new type of classifier called naive typicality-based EDA class is introduced, which is based on the newly introduced global typicality. This is only one of the wide range of possible applications of EDA including but not limited for anomaly detection, clustering, classification, control, prediction, control, rare events analysis, etc., which will be the subject of further research.

Rare events analysis of temperature chaos in the Sherrington–Kirkpatrick model

We investigate the question of temperature chaos in the Sherrington–Kirkpatrick spin glass model, applying a recently proposed rare events based data analysis method to existing Monte Carlo data. Thanks to this new method, temperature chaos is now observable for this model, even with the limited size systems that can currently be simulated.

Rare Events Analysis for High-Frequency Equity Data

In this work we present a methodology to detect rare events which are defined as large price movements relative to the volume traded. We analyze the behavior of equity after the detection of these rare events. We provide methods to calibrate trading rules based on the detection of these events and illustrate for a particular trading rule. We apply the methodology to tick data for thousands of equities over a period of five days. In order to draw comprehensive conclusions, we group the equities into classes and calculate probabilities of price recovery after these rare events for each class. The methodology that we have developed is based on non-parametric statistics and makes no assumption about the distribution of the random variables in the study. Copyright © 2011 Wilmott Magazine Ltd.

MULTISCALE COMPUTATIONS ON NEURAL NETWORKS: FROM THE INDIVIDUAL NEURON INTERACTIONS TO THE MACROSCOPIC-LEVEL ANALYSIS

We show how the Equation-Free approach for multiscale computations can be exploited to systematically study the dynamics of neural interactions on a random regular connected graph under a pairwise representation perspective. Using an individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of Simulated Annealing we compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states, sidestepping the necessity of obtaining explicit closures at the macroscopic level. We also exploit the scheme to perform a rare-events analysis by estimating an effective Fokker–Planck equation describing the evolving probability density function of the corresponding coarse-grained observables.

DETECTION AND PREDICTION OF RARE EVENTS IN TRANSACTION DATABASES

Rare events analysis is an area that includes methods for the detection and prediction of events, e.g. a network intrusion or an engine failure, that occur infrequently and have some impact to the system. There are various methods from the areas of statistics and data mining for that purpose. In this article we propose PREVENT, an algorithm which uses inter-transactional patterns for the prediction of rare events in transaction databases. PREVENT is a general purpose inter-transaction association rules mining algorithm that optimally fits the demands of rare event prediction. It requires only 1 scan on the original database and 2 over the transformed, which is considerably smaller and it is complete as it does not miss any patterns. We provide the mathematical formulation of the problem and experimental results that show PREVENT's efficiency in terms of run time and effectiveness in terms of sensitivity and specificity.

Rare events in queueing systems?A survey

Several practical approaches have been used to estimate the probabilities of rare events occurring in queueing processes. Rare events of practical interest can be considered as large deviations for a fixed queueing process (such as level crossing by the waiting time, or the queue length) or as those for a limiting triangular scheme. This paper is a survey of the literature devoted to the regenerative analysis of rare events. Because of the importance of busy period parameters, rare events within a busy period are discussed. A number of small parameter theorems useful in rare events analysis are outlined, including singular states aggregation theorems. Simulation methods for rare events analysis and other numerical methods are also presented.