Applied Stochastic Models in Business and Industry 

About: Applied Stochastic Models in Business and Industry is an academic journal published by Wiley-Blackwell. The journal publishes majorly in the area(s): Computer science & Bayesian probability. It has an ISSN identifier of 1524-1904. Over the lifetime, 1357 publications have been published receiving 16976 citations.

Applications of Hilbert–Huang transform to non-stationary financial time series analysis

Abstract: A new method, the Hilbert–Huang Transform (HHT), developed initially for natural and engineering sciences has now been applied to financial data. The HHT method is specially developed for analysing non-linear and non-stationary data. The method consists of two parts: (1) the empirical mode decomposition (EMD), and (2) the Hilbert spectral analysis. The key part of the method is the first step, the EMD, with which any complicated data set can be decomposed into a finite and often small number of intrinsic mode functions (IMF). An IMF is defined here as any function having the same number of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima, and minima respectively. The IMF also thus admits well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to non-linear and non-stationary processes. With the Hilbert transform, the IMF yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy–frequency–time distribution, which we designate as the Hilbert Spectrum. Comparisons with Wavelet and Fourier analyses show the new method offers much better temporal and frequency resolutions. The EMD is also useful as a filter to extract variability of different scales. In the present application, HHT has been used to examine the changeability of the market, as a measure of volatility of the market. Published in 2003 by John Wiley & Sons, Ltd.

Stochastic modelling and analysis of degradation for highly reliable products

Abstract: Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision-makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn-in modelling will also be discussed. Copyright © 2014 John Wiley & Sons, Ltd.

A modern Bayesian look at the multi-armed bandit

Abstract: A multi-armed bandit is an experiment with the goal of accumulating rewards from a payoff distribution with unknown parameters that are to be learned sequentially. This article describes a heuristic for managing multi-armed bandits called randomized probability matching, which randomly allocates observations to arms according the Bayesian posterior probability that each arm is optimal. Advances in Bayesian computation have made randomized probability matching easy to apply to virtually any payoff distribution. This flexibility frees the experimenter to work with payoff distributions that correspond to certain classical experimental designs that have the potential to outperform methods that are ‘optimal’ in simpler contexts. I summarize the relationships between randomized probability matching and several related heuristics that have been used in the reinforcement learning literature. Copyright © 2010 John Wiley & Sons, Ltd.

Analysis of regression in game theory approach

Abstract: Working with multiple regression analysis a researcher usually wants to know a comparative importance of predictors in the model. However, the analysis can be made difficult because of multicollinearity among regressors, which produces biased coefficients and negative inputs to multiple determination from presum ably useful regressors. To solve this problem we apply a tool from the co-operative games theory, the Shapley Value imputation. We demonstrate the theoretical and practical advantages of the Shapley Value and show that it provides consistent results in the presence of multicollinearity. Copyright © 2001 John Wiley & Sons, Ltd.

A tutorial on ν‐support vector machines

Abstract: We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.