Applied Stochastic Models in Business and Industry最新文献

This article corrects the following:

In this research paper by Chew et al.,¹ on page 590, the funding information in the Acknowledgement is incorrect.

The correct funding information should be:

This work is funded by the Ministry of Higher Education Malaysia, Fundamental Research Grant Scheme [Grant Number: FRGS/1/2019/STG06/USM/02/5], for the project entitled “New Robust Adaptive Model for Coefficient of Variation in Infinite and Finite Horizon Processes.”

We apologise for this error.

We response to comments on our paper “Specifying Prior Distributions in Reliability Applications” in this rejoinder.

The ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold-based monitoring system named self-monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs' remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi-state models to the resulting semi-competing risks data. The multi-state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time-dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions.

A system of $��n�$ components is here considered, with component deterioration modeled by non decreasing time-scaled Lévy processes. When a component fails, a sudden change in the time-scaling functions of the surviving components is induced, which makes the components stochastically dependent. We compute the reliability function of coherent systems under this new dependence model. We next study the distribution of the ordered failure times, and establish some positive dependence properties. We also provide stochastic comparison results in the usual multivariate stochastic order between failure times of two dependence models with different parameters. Finally, some numerical experiments illustrate the theoretical results.

We propose a real options model with regime shifts to investigate the effect of cutting interest rates on corporate investments when a financial crisis occurs. Cutting interest rates would lower the investment project's hurdle rate. The reduction in hurdle rate is positively related to the magnitude of interest rate cuts and the persistence of the financial crisis. The hurdle rate becomes lower in the financial crisis state because the reduction in interest rate would lower the cost of capital and the opportunity cost of immediate investment. In the numerical analysis of this study, we show that the change in the opportunity cost accounts for most of the change in the hurdle rate. Upon taking into consideration the firm's financing constraints, we find that cutting interest rates accelerates investments for firms with high liquidity. However, for firms with low liquidity, the optimal investment threshold is not affected by the variation in interest rates. Instead, the investments of low-liquidity firms are affected by the change in the friction of credit supply.

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed-forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.

It has been recognized that model risk has an important effect on any risk measurement procedures, particularly when dealing with complex markets and in the presence of a wide range of implemented models. We consider a normalized measure of model risk for the forecast of daily Value-at-Risk, combined with a model selection and an averaging procedure. This allows us to restrict the set of plausible models on a daily basis, making the initial choice of competing models less crucial and then yielding a more reliable assessment of model risk. Using AR-GARCH-type models with different distributions for the innovations, we assess the dynamics of model risk for different financial assets (a stock, an equity index, an exchange rate) and commodities (electricity, crude oil and natural gas) over 15 years.

The comparison of coherent systems in terms of stochastic orders is vital in reliability theory. While there is a considerable amount of literature devoted to comparing systems with homogeneous and independent components, real-world systems often consist of heterogeneous components. Hence, this article aims to investigate systems with heterogeneous and independent components, as well as, those with heterogeneous and dependent components. For this purpose, we consider systems comprise of three components, which are of two different types of components, namely two components of type A and one component of type B. The system's lifetime distribution is represented using the failure signature when the components are independent, which is a function of the component's life distribution. However, when the components are dependent, the system's lifetime distribution is represented using copula and diagonal sections. Additionally, distorted distributions are utilized to enable distribution-free stochastic comparisons. Using these representations, we compare systems with components having proportional reversed hazard rates, in three scenarios: (i) when components are heterogeneous and independent; (ii) when components are heterogeneous and dependent; and finally, (iii) comparing systems with homogeneous and independent components with those that have heterogeneous components. To illustrate the applicability of these results, we provide some examples and applications.

Commodity price volatility is a major source of instability in those countries that are primarily commodity-dependent and has a negative impact, especially on economic growth. With this premise, commodities represent an effective financial exchange tool that nowadays finds relevance in being involved in the processes inherent to environmental sustainability. This work focus on raw materials and their demand, connected with the need for a transition towards the Circular Economy, as part of a strategy to address commodity supply disruptions. It presupposes changes in the mentality and behavior of companies in the various economic sectors. A crucial issue debated in the literature concerns whether or not the size of the companies favors their attitude towards Circular Economy. We propose a nonparametric method to test the effect of firm size on their propensity to undertake Circular Economy activities. Considering $��k�$ of such activities, this propensity is a multidimensional concept and it can be represented by a $��k�$-dimensional vector of proportions. Each element of the vector represents the share of companies of the population under study that implement a specific Circular Economy activity. The main difficulty of such a multivariate testing problem, together with the multidimensional nature of the dichotomous response, is the one-sided type alternative, which is a stochastic dominance for multidimensional binary variables. A Monte Carlo simulation study proves the good power behavior of the proposed solution, based on a nonparametric approach. Case studies related to Italian small and medium enterprises in some strategic sectors are also addressed.

We propose a reinforcement learning (RL) algorithm for generating a trading strategy in a realistic setting, that includes transaction costs and factors driving the asset dynamics. We benchmark our algorithm against the analytical optimal solution, available when factors are linear and transaction costs are quadratic, showing that RL is able to mimic the optimal strategy. Then we consider a more realistic setting, including non-linear dynamics, that better describes the WTI spot prices time series. For these more general dynamics, an optimal strategy is not known and RL becomes a viable alternative. We show that on synthetic data generated from WTI spot prices, the RL agent outperforms a trader that linearizes the model to apply the theoretical optimal strategy.