Applied Stochastic Models in Business and Industry

Bhattacharya, R.; Pradhan, B.; Dewanji, A.

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.1997

Hybrid censoring scheme is a combination of Type‐I and Type‐II censoring schemes. Determination of optimum hybrid censoring scheme is an important practical issue in designing life testing experiments to enhance the information on reliability of the product. In this work, we consider determination of optimum life testing plans under hybrid censoring scheme by minimizing the total cost associated with the experiment. It is shown that the proposed cost function is scale invariant for some selected distributions. Optimum solution cannot be obtained analytically. We propose a method for obtaining the optimum solution and consider Weibull distribution for illustration. We also studied the sensitivity of the optimal solution to the misspecification of parameter values and cost components through a well‐designed sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.

De Leonardis, Daniele; Rocci, Roberto

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.1998

Cure models represent an appealing tool when analyzing default time data where two groups of companies are supposed to coexist: those which could eventually experience a default (uncured) and those which could not develop an endpoint (cured). One of their most interesting properties is the possibility to distinguish among covariates exerting their influence on the probability of belonging to the populations’ uncured fraction, from those affecting the default time distribution. This feature allows a separate analysis of the two dimensions of the default risk: whether the default can occur and when it will occur, given that it can occur. Basing our analysis on a large sample of Italian firms, the probability of being uncured is here estimated with a binary logit regression, whereas a discrete time version of a Cox's proportional hazards approach is used to model the time distribution of defaults. The extension of the cure model as a forecasting framework is then accomplished by replacing the discrete time baseline function with an appropriate time‐varying system level covariate, able to capture the underlying macroeconomic cycle. We propose a holdout sample procedure to test the classification power of the cure model. When compared with a single‐period logit regression and a standard duration analysis approach, the cure model has proven to be more reliable in terms of the overall predictive performance. Copyright © 2013 John Wiley & Sons, Ltd.

Labbé, Chantal; Watier, François

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2002

In this paper, we establish closed‐form formulas for key probabilistic properties of the cone‐constrained optimal mean‐variance strategy, in a continuous market model driven by a multidimensional Brownian motion and deterministic coefficients. In particular, we compute the probability to obtain to a point, during the investment horizon, where the accumulated wealth is large enough to be fully reinvested in the money market, and safely grow there to meet the investor's financial goal at terminal time. We conclude that the result of Li and Zhou [Ann. Appl. Prob., v.16, pp.1751–1763, (2006)] in the unconstrained case carries over when conic constraints are present: the former probability is lower bounded by 80% no matter the market coefficients, trading constraints, and investment goal. We also compute the expected terminal wealth given that the investor's goal is underachieved, for both the mean‐variance strategy and the aforementioned hybrid strategy where transfer to the money market occurs if it allows to safely achieve the goal. The former probabilities and expectations are also provided in the case where all risky assets held are liquidated if financial distress is encountered. These results provide investors with novel practical tools to support portfolio decision‐making and analysis. Copyright © 2013 John Wiley & Sons, Ltd.

Bersimis, S.; Chalkias, C.; Anthopoulou, T.

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2003

The primary aim of this paper is to expose the use and the value of spatial statistical analysis in business and especially in designing economic policies in rural areas. Specifically, we aim to present under a unified framework, the use of both point and area‐based methods, in order to analyze in‐depth economic data, as well as, to drive conclusions through interpreting the analysis results. The motivating problem is related to the establishment of women‐run enterprises in a rural area of Greece. Moreover, in this article, the spatial scan statistic is successfully applied to the spatial economic data at hand, in order to detect possible clusters of small women‐run enterprises in a rural mountainous and disadvantaged region of Greece. Then, it is combined with Geographical Information System based on Local Indicator of Spatial Autocorrelation scan statistic for further exploring and interpreting the spatial patterns. The rejection of the random establishment of women‐run enterprises and the interpretation of the clustering patterns are deemed necessary, in order to assist government in designing policies for rural development. Copyright © 2014 John Wiley & Sons, Ltd.

Weiß, Christian H.; Kim, Hee‐Young

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2005

This article considers the modeling of count data time series with a finite range having extra‐binomial variation. We propose a beta‐binomial autoregressive model using the concept of random coefficient thinning. We discuss the stationarity conditions, derive the moments and autocovariance function and consider approaches for parameter estimation. Furthermore, we develop two new tests for detecting extra‐binomial variation, and we derive the asymptotic distributions of the test statistics under the null hypothesis of a binomial autoregressive model. The size and power performance of the two tests are analyzed under various alternatives taken from a beta‐binomial autoregressive model with Monte Carlo experiments. The article ends with a real‐data example about the Harmonised Index of Consumer Prices of the European Union. Copyright © 2013 John Wiley & Sons, Ltd.

Gallizo, José Luis; Gargallo, Pilar; Saladrigues, Ramon; Salvador, Manuel

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2034

This study offers an exploratory statistical analysis of the persistence of annual abnormal returns across a sample of firms from different European Union countries. To this end, a hierarchical Bayesian dynamic model has been used that enables the annual behaviour of those profits to be broken down into a permanent structural component and a transitory component, while also distinguishing between general effects affecting the industry as a whole and specific effects impacting on each firm in particular. This breakdown of the behaviour of profits allows for a more accurate assessment of the relative importance of these fundamental components by country and sector. Furthermore, through the Bayesian approach, it is possible to test different hypotheses about the homogeneity of the dynamic behaviour of the aforementioned components with respect to the sector and the country where the firm develops its activity.

Skudlarek, Stefan Jan; Yamamoto, Hirosuke

2014 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2057

Anomaly detection within non‐numerical sequence data has developed into an important topic of data mining, but comparatively little research has been done regarding anomaly detection without training data (unsupervised anomaly detection). One application found in computer security is the detection of a so‐called masquerade attack, which consists of an attacker abusing a regular account. This leaves only the session input, which is basically a string of non‐numerical commands, for analysis. Our previous approach to this problem introduced the use of the so‐called average index difference function for mapping the non‐numerical symbol data to a numerical space. In the present paper, we examine the theoretical properties of the average index difference function, present an enhanced unsupervised anomaly detection algorithm based on the average index difference function, show the parameters to be theoretically inferable, and evaluate the performance using real‐world data. Copyright © 2014 John Wiley & Sons, Ltd.