Search results for "Order statistic"

Corrigendum to three papers that deal with “Anti”-Bayesian Pattern Recognition [Pattern Recognition]

2014

In the papers 1 (Thomas and Oommen, 2013), 2 (Oommen and Thomas, 2014) and 3 (Thomas and Oommen, 2013), and their associated conference versions cited in those papers, we had introduced a new method of so-called "Anti"-Bayesian Pattern Recognition (PR) which achieved the classification using only a few (sometimes as few as two) points distant from the mean. While the PR strategy, in and of itself, is accurate, the claim that it was based on the Order Statistics (OS) of the distributions of the features is not. The PR and classification results are rather founded on the symmetric quantiles and not on the symmetric OSs. This brief paper corrects the flawed claim presented in those papers. Hig…

Finding Prediction Limits for a Future Number of Failures in the Prescribed Time Interval under Parametric Uncertainty

2012

Computing prediction intervals is an important part of the forecasting process intended to indicate the likely uncertainty in point forecasts. Prediction intervals for future order statistics are widely used for reliability problems and other related problems. In this paper, we present an accurate procedure, called ‘within-sample prediction of order statistics', to obtain prediction limits for the number of failures that will be observed in a future inspection of a sample of units, based only on the results of the first in-service inspection of the same sample. The failure-time of such units is modeled with a two-parameter Weibull distribution indexed by scale and shape parameters β and δ, …

Thompson Sampling for Dynamic Multi-armed Bandits

2011

The importance of multi-armed bandit (MAB) problems is on the rise due to their recent application in a large variety of areas such as online advertising, news article selection, wireless networks, and medicinal trials, to name a few. The most common assumption made when solving such MAB problems is that the unknown reward probability theta k of each bandit arm k is fixed. However, this assumption rarely holds in practice simply because real-life problems often involve underlying processes that are dynamically evolving. In this paper, we model problems where reward probabilities theta k are drifting, and introduce a new method called Dynamic Thompson Sampling (DTS) that facilitates Order St…

An Analysis of Earthquakes Clustering Based on a Second-Order Diagnostic Approach

2009

A diagnostic method for space–time point process is here introduced and applied to seismic data of a fixed area of Japan. Nonparametric methods are used to estimate the intensity function of a particular space–time point process and on the basis of the proposed diagnostic method, second-order features of data are analyzed: this approach seems to be useful to interpret space–time variations of the observed seismic activity and to focus on its clustering features.

The variance of the $\ell _p^n$-norm of the Gaussian vector, and Dvoretzky’s theorem

2019

Some properties of local weighted second-order statistics for spatio-temporal point processes

2019

Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into residuals as a result of a thinning or a rescaling procedure. We alternatively consider here second-order statistics coming from weighted measures. Motivated by Adelfio and Schoenberg (Ann Inst Stat Math 61(4):929–948, 2009) for the temporal and spatial cases, we consider an extension to the spatio-temporal context in addition to focussing on local characteristics. In particular, our proposed method assesses goodness-of-fit of spatio-temporal models by using local weighted second-order statistics, computed after weighting the contribution of each observed point by the…

Order statistics-based parametric classification for multi-dimensional distributions

2013

Traditionally, in the field of Pattern Recognition (PR), the moments of the class-conditional densities of the respective classes have been used to perform classification. However, the use of phenomena that utilized the properties of the Order Statistics (OS) were not reported. Recently, in [10,8], we proposed a new paradigm named CMOS, Classification by the Moments of Order Statistics, which specifically used these quantifiers. It is fascinating that CMOS is essentially ''anti''-Bayesian in its nature because the classification is performed in a counter-intuitive manner, i.e., by comparing the testing sample to a few samples distant from the mean, as opposed to the Bayesian approach in whi…

Large eddy simulations on the effect of the irregular roughness shape on turbulent channel flows

2019

Abstract Large Eddy Simulations (LES) are carried out to investigate on the mean flow in turbulent channel flows over irregular rough surfaces. Here the attention is focused to selectively investigate on the effect induced by crests or cavities of the roughness. The irregular shape is generated through the super-imposition of sinusoidal functions having random amplitude and four different wave-lengths. The irregular roughness profile is reproduced along the spanwise direction in order to obtain a 2D rough shape. The analysis of the mean velocity profiles shows that roughness crests induce higher effect in the outer-region whereas roughness cavities cause the highest effects in the inner-reg…

Evolving Tree Algorithm Modifications

2007

There are many variants of the original self-organizing neural map algorithm proposed by Kohonen. One of the most recent is the Evolving Tree, a tree-shaped self-organizing network which has many interesting characteristics. This network builds a tree structure splitting the input dataset during learning. This paper presents a speed-up modification of the original training algorithm useful when the Evolving Tree network is used with complex data as images or video. After a measurement of the effectiveness an application of the modified algorithm in image segmentation is presented.

A New Dynamic Model for Anticipatory Adaptive Control of Airline Seat Reservation via Order Statistics of Cumulative Customer Demand

2017

This paper deals with dynamic anticipatory adaptive control of airline seat reservation for the stochastic customer demand that occurs over time T before the flight is scheduled to depart. It is assumed that time T is divided into m periods, namely a full fare period and m−1 discounted fare periods. The fare structure is given. An airplane has a seat capacity of U. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flight with two fare classes (business and economy). The proposed policies of the airline seat inventory control are based on the use of order statistics of cumulative customer demand, which have such properties as bivar…