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Estimation with extended sequential order statistics: A link function approach Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-03-25 Tim Pesch, Erhard Cramer, Adriano Polpo, Edward Cripps
The model of extended sequential order statistics (ESOS) comprises of two valuable characteristics making the model powerful when modelling multi‐component systems. First, components can be assumed to be heterogeneous and second, component lifetime distributions can change upon failure of other components. This degree of flexibility comes at the cost of a large number of parameters. The exact number
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Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-03-18 Shanya Baghel, Shuvashree Mondal
Recently, a growing interest is evident in modelling dependent competing risks in lifetime prognosis problems. In this work, we propose to model the dependent competing risks by Marshal‐Olkin bivariate exponential distribution. The observable data consists of a number of failures due to different causes across different time intervals. The failure count data is common in instances like one‐shot devices
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The censored delta shock model with non‐identical intershock times distribution and an optimal replacement policy Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-03-11 Stathis Chadjiconstantinidis
In this article, we consider the censored model in which the distribution of intershock times do not have the same distribution, but it is assumed that a change occurs in the distribution of the intershock times due to an environmental effect and hence this distribution changes after a random number of shocks. For this shock model, several reliability characteristics are evaluated by assuming that
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Preface to the special issue on degradation and maintenance, modelling and analysis Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-03-11 Laurent Doyen, Inma T. Castro
The 13th spring meeting of the ENBIS society was held in Grenoble, France, on May 19–20 2022. ENBIS is the European Network for Business and Industrial Statistics. Its objective is to connect people and organisations throughout Europe to improve statistical methods and their applications in the field of business and industry. ENBIS organizes each year a one-week congress and a 2- or 3-days spring meeting
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Preventive maintenance for coherent systems considering postponed replacement Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-03-01 Majid Asadi
One primary objective of reliability engineering is to achieve optimal maintenance of technical systems, which ensures they remain in good operating condition. This paper proposes an age‐based preventive optimal maintenance policy for ‐component coherent systems. Under this proposed strategy, the system begins operating at and undergoes preventative maintenance (PM) at a time . If the system fails
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The impact of TV advertising on website traffic Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-02-19 Lukáš Veverka, Vladimír Holý
We propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the nonlinear dependence of the estimated increase
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Imperfect and worse than old maintenances for a gamma degradation process Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-02-16 Franck Corset, Mitra Fouladirad, Christian Paroissin
This article considers a condition-based maintenance for a system subject to deterioration. The deterioration is modeled by a non-homogeneous gamma process, more precisely the gamma process and the preventive maintenance are imperfect or worse than old. The corrective maintenance actions are as good as new. The maintenance efficiency or non-efficiency parameters as well as the deterioration parameters
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Modeling flight delays by an intensity-based Hawkes process Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-02-13 Philip Hans Franses, Carlijn Smeets
Two key features of airline departure delays are that they cascade and that there can be exceptional peaks. We model these features using an intensity-based Hawkes process. Our application to all KLM departure delays at Amsterdam Schiphol airport in January 2015 shows that volatility in departure delays is endogenous. We correlate the key parameters of the estimated Hawkes process with daily weather
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Resale regulations in online marketplaces during the COVID-19 pandemic Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-29 Yohsuke Hirose
In the early stages of the COVID-19 pandemic, masks and alcohol sanitizers were hoarded and resold in online markets. In Japan, restrictions were imposed on such resale. This paper examines changes in the behavior of economic agents and their surplus before and after the resale restrictions in the online market. We collected data on the auctions and fixed-price sales of anhydrous ethanol before and
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New tests for trend in time censored recurrent event data Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-28 Bo Henry Lindqvist, Jan Terje Kvaløy
We consider testing for trend in recurrent event data. More precisely, for such data we consider testing of the null hypothesis of data coming from a renewal process. The new tests are essentially obtained by considering appropriate integrated versions of classical trend tests. Moreover, adaptive versions of earlier considered tests versus non-monotonic alternatives, like bathtub trend, are suggested
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Multivariate simulation-based forecasting for intraday power markets: Modeling cross-product price effects Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-25 Simon Hirsch, Florian Ziel
Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between
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Linkage vector autoregressive model Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-16 Manabu Asai, Mike K. P. So
To accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical data, where unknown parameters of the function can be
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An initial investigation for employing ACH depth function in degradation model selection: A case study with real data Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-14 Arefe Asadi, Mitra Fouladirad, Diego Tomassi
In degradation modeling, stochastic processes often do not meet the classical properties necessary for traditional goodness-of-fit tests. This paper presents an initial investigation into employing the ACH depth function and its potential in degradation model selection. We commence by presenting various stochastic processes as degradation models and their selection criteria. Subsequently, we delve
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Demand for live betting: An analysis using state-space models Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-04 Marius Ötting, Rouven Michels, Roland Langrock, Christian Deutscher
Sports betting markets have grown very rapidly recently, with the total European gambling market worth 98.6 billion euro in 2019. Considering a high-resolution (1 Hz) data set provided by a large European bookmaker, we investigate the demand for bet placements during matches and in particular the effect of news. Accounting for the general market activity level within a state-space modelling framework
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Bayesian analysis of Markov modulated queues with abandonment Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-04 Atilla Ay, Joshua Landon, Süleyman Özekici, Refik Soyer
We consider a Markovian queueing model with abandonment where customer arrival, service and abandonment processes are all modulated by an external environmental process. The environmental process depicts all factors that affect the exponential arrival, service, and abandonment rates. Moreover, the environmental process is a hidden Markov process whose true state is not observable. Instead, our observations
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Optimal maintenance policy for imperfect production systems using reliability function and defect rate Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2024-01-04 Jyh-Wen Ho, Yeu-Shiang Huang, Peng-Tsi Huang
This study examines how a manufacturer can improve the robustness of an imperfect production system by implementing maintenance activities, which can be measured in terms of system reliability and product failure rate. A two-dimensional maintenance policy comprising system reliability and the product defect rate is proposed to assess maintenance activity costs. The optimal thresholds of the two dimensions
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A dam management problem with energy production as an optimal switching problem Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-29 Etienne Chevalier, Cristina Di Girolami, M'hamed Gaïgi, Elisa Giovannini, Simone Scotti
We consider an optimal stochastic control problem for a dam. Electrical power production is operating under an uncertain setting for electricity market prices and water level which has to be kept under control. Indeed, the water level inside the basin cannot exceed a certain threshold for safety reasons, and at the same time cannot decrease below another threshold in order to keep power production
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Correction to deep reinforcement learning-based ordering mechanism for performance optimization in multi-echelon supply chains Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-28 Dony S. Kurian, V. Madhusudanan Pillai
This paper addresses and acknowledges the valuable feedback provided by Dr. Deniz Preil in response to the recent study conducted by Kurian et al which investigates the application of proximal policy optimization (PPO) to determine dynamic ordering policies within multi-echelon supply chains. The first comment raised by Dr. Preil motivated an examination of the training and evaluation procedures in
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Bayesian change point prediction for downhole drilling pressures with hidden Markov models Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-19 Ochuko Erivwo, Viliam Makis, Roy Kwon
In the drilling of oil wells, the need to accurately detect downhole formation pressure transitions has long been established as critical for safety and economics. In this article, we examine the application of Hidden Markov Models (HMMs) to oilwell drilling processes with a focus on the real time evolution of downhole formation pressures in its partially observed state. The downhole drilling pressure
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Erratum to “An improved Hotelling's T2 chart for monitoring a finite horizon process based on run rules schemes: A Markov-chain approach” Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-12
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
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Rejoinder to “Specifying Prior Distribution in Reliability Applications” Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-07 Qinglong Tian, Colin Lewis-Beck, Jarad B. Niemi, William Q. Meeker
We response to comments on our paper “Specifying Prior Distributions in Reliability Applications” in this rejoinder.
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Examining the impact of critical attributes on hard drive failure times: Multi-state models for left-truncated and right-censored semi-competing risks data Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-12-03 Jordan L. Oakley, Matthew Forshaw, Pete Philipson, Kevin J. Wilson
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
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A model for stochastic dependence implied by failures among deteriorating components Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-11-15 Emilio Casanova Biscarri, Sophie Mercier, Carmen Sangüesa
A system of n $$ n $$ 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
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The effect of cutting interest rates on corporate investments: A real options model Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-11-01 Nan-Wei Han, Mao-Wei Hung, I-Shin Wu
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
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Deep generative models for vehicle speed trajectories Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-10-26 Farnaz Behnia, Dominik Karbowski, Vadim Sokolov
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
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Assessing model risk in financial and energy markets using dynamic conditional VaRs Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-10-14 Angelica Gianfreda, Giacomo Scandolo
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
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Comparisons of coherent systems with two types of heterogeneous components having proportional reversed hazard rates Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-10-14 T. V. Rao, Sameen Naqvi
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
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Simultaneous marginal homogeneity versus directional alternatives for multivariate binary data with application to circular economy assessments Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-10-04 Stefano Bonnini, Michela Borghesi, Massimiliano Giacalone
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
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A reinforcement learning algorithm for trading commodities Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-10-03 Federico Giorgi, Stefano Herzel, Paolo Pigato
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
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Misspecification analysis of gamma- and inverse Gaussian-based perturbed degradation processes Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-28 Nicola Esposito, Agostino Mele, Bruno Castanier, Massimiliano Giorgio
Albeit not equivalent, in many applications the gamma and the inverse Gaussian processes are treated as if they were. This circumstance makes the misspecification problem of these models interesting and important, especially when data are affected by measurement errors, since noisy/perturbed data do not allow to verify whether the selected model is actually able to adequately fit the real (hidden)
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Forecasting system's accuracy: A framework for the comparison of different structures Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-19 Carla Freitas Silveira Netto, Vinicius A. Brei, Rob J. Hyndman
One of the most challenging aspects for managers when building a forecasting system is choosing how to aggregate the data at different levels. This is frequently done without the manager knowing how these choices can compromise the system's accuracy. This article illustrates these compromises by comparing different structures and aggregation criteria. Our article proposes and empirically tests a framework
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Stochastic comparisons of coherent systems with active redundancy at the component or system levels and component lifetimes following the accelerated life model Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-12 Arindam Panja, Pradip Kundu, Biswabrata Pradhan
An effective way to increase system reliability is to use redundancies (spares) into the systems either in component level or in system level. In this prospect, it is a significant issue that which set of available spares providing better system reliability in some stochastic sense. In this paper, we derive sufficient conditions under which a coherent system with a set of active redundancy at the component
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On the multiattempt minimal repair and the corresponding counting process Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-13 Ji Hwan Cha, Maxim Finkelstein
Minimally repaired items are considered. In practice, minimal repair can be unsuccessful, and in this case, it should be repeated. The Polya-Aeppli process, which is a generalization of the Poisson process is used in the article for the corresponding modeling. Some properties, useful for optimal maintenance, are derived. An important generalization to the case when the probability of the unsuccessful
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Hedging temperature risk with CDD and HDD temperature futures Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-06 Fred Espen Benth, Jukka Lempa
This paper is concerned with managing risk exposure to temperature using weather derivatives. We consider hedging temperature risk using so-called HDD- and CDD-index futures, which are instruments written on temperatures in specific locations over specific time periods. The temperatures are modelled as continuous-time autoregressive (CARMA) processes and pricing of the hedging instrument is done under
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Discussion of Specifying prior distributions in reliability applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-06 Maria Kateri
Congratulations on this great and comprehensive achievement. Undoubtedly, Bayesian inference plays an increasingly important role in reliability data analysis, dictated on the one hand by the usually small sample sizes per experimental condition, which bring standard frequentist procedures to their limits, and on the other hand by the fact that uncertainty quantification and communication are more
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An internal fraud model for operational losses in retail banking Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-06 Rocío Paredes, Marco Vega
This article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational
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Discussion specifying prior distributions in reliability applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-09-03 Alfonso Suárez-Llorens
1 DISCUSSION Firstly, I want to congratulate the authors in Reference 1 for their practical contextualization in describing the Bayesian method in real-world problems with reliability data. Undoubtedly, one of the main strengths of this article is its highly practical approach, starting from real situations and examples, and showing why Bayesian inference is many times a nice alternative for making
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Discussion of “specifying prior distribution in reliability applications” by Tian, Lewis-Beck, Niemi, and Meeker Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-22 Rong Pan
Reliability prediction is often plagued by small sample size; thus, Bayesian inference methods have been sought to rescue. This article, hereafter referred to as the TLNM paper, gives a systematic review of how to select and specify prior distributions for Bayesian reliability prediction and provides many excellent suggestions to reliability engineers and Bayesian practitioners. I congratulate the
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Value-at-Risk with quantile regression neural network: New evidence from internet finance firms Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-18 Li Zeng, Wee-Yeap Lau, Elya Nabila Abdul Bahri
Traditional risk measurements have proven inadequate in capturing tail risk and nonlinear correlation. This study proposes a novel approach to measure financial risk in the Internet finance industry: a new Value-at-Risk (VaR) measurement based on quantile regression neural network (QRNN). Sparrow Search Algorithm (SSA) is utilized to optimize the QRNN model, which improves the model's performance in
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A general framework for optimal stopping problems with two risk factors and real option applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-14 Rossella Agliardi
A new explicit solution is obtained for a general class of two-dimensional optimal stopping problems arising in real option theory. First, the solvable case of homogeneous and quasi-homogeneous problems is presented in a comprehensive framework. Then the general problem—including the unsolved case of inhomogeneous functions—is considered and an explicit expression for the value function is obtained
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Quantum Bayesian computation Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-14 Nick Polson, Vadim Sokolov, Jianeng Xu
Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep
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Bootstrapping through discrete convolutional methods Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-09 Jared M. Clark, Richard L. Warr
Bootstrapping was designed to randomly resample data from a fixed sample using Monte Carlo techniques. However, the original sample itself defines a discrete distribution. Convolutional methods are well suited for discrete distributions, and we show the advantages of utilizing these techniques for bootstrapping. The discrete convolutional approach can provide exact numerical solutions for bootstrap
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Lost in a black-box? Interpretable machine learning for assessing Italian SMEs default Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-08-07 Lisa Crosato, Caterina Liberati, Marco Repetto
Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black-box type of models. In order to overcome this drawback and maintain the high performances of black-boxes, this paper has
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Discussion of “Specifying prior distributions in reliability applications” Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-30 Lizanne Raubenheimer
The authors should be congratulated on a very interesting and insightful discussion which motivates the use of Bayesian inference in reliability theory. The article motivates the use of Bayesian methods especially in the case of small number of failures. The following log-location-scale distributions are considered: the lognormal distribution and the Weibull distribution. The importance of reparameterization
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Time-invariant portfolio strategies in structured products with guaranteed minimum equity exposure Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-30 Luca Di Persio, D. Mancinelli, Immacolata Oliva, K. Wallbaum
We introduce a new exotic option to be used within structured products to address a key disadvantage of standard time-invariant portfolio protection: the well-known cash-lock risk. Our approach suggests enriching the framework by including a threshold in the allocation mechanism so that a guaranteed minimum equity exposure (GMEE) is ensured at any point in time. To be able to offer such a solution
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Discussion of “some statistical challenges in automated driving systems” Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-24 Feng Guo
The emergence of automated driving systems (ADS) has been a remarkable technological leap in recent times, holding tremendous potential to revolutionize mobility, minimize energy usage, and enhance safety on our roads. The present paper serves as a valuable contribution, addressing crucial aspects that underscore the significance of statistics in ADS applications and the accompanying challenges. I
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Modeling clustered binary data with nonparametric unobserved heterogeneity: An application to stock crash analysis Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-21 Ruixi Zhao, Renjun Ma, Guohua Yan, Haomiao Niu, Wenjiang Jiang
Various random effects models have been developed for clustered binary data; however, traditional approaches to these models generally rely heavily on the specification of a continuous random effect distribution such as Gaussian or beta distribution. In this article, we introduce a new model that incorporates nonparametric unobserved random effects on unit interval (0,1) into logistic regression multiplicatively
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A real-time monitoring approach for bivariate event data Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-20 Inez Maria Zwetsloot, Tahir Mahmood, Funmilola Mary Taiwo, Zezhong Wang
Early detection of changes in the frequency of events is an important task in many fields, such as disease surveillance, monitoring of high-quality processes, reliability monitoring, and public health. This article focuses on detecting changes in multivariate event data by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited because they only signal after an event
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Discussion of specifying prior distributions in reliability applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-13 Frank P.A. Coolen
The paper Specifying Prior Distributions in Reliability Applications mainly provides an overview of methods for selecting non-informative prior distributions for parameters of basic lifetime distributions, as often used in reliability analyses. This discussion raises some related issues and comments on opportunities beyond basic Bayesian statistical methods which may be useful in reliability scenarios
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Modelling the Bitcoin prices and media attention to Bitcoin via the jump-type processes Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-13 Ekaterina Morozova, Vladimir Panov
In this paper, we present a new bivariate model for the joint description of the Bitcoin prices and the media attention to Bitcoin. Our model is based on the class of the Lévy processes and is able to realistically reproduce the jump-type dynamics of the considered time series. We focus on the low-frequency setup, which is for the Lévy-based models essentially more difficult than the high-frequency
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On the efficacy of “herd behavior” in the commodities market: A neuro-fuzzy agent “herding” on deep learning traders Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-04 Alfonso Guarino, Luca Grilli, Domenico Santoro, Francesco Messina, Rocco Zaccagnino
This article analyzes the trading strategies of five state-of-the-art agents based on reinforcement learning on six commodity futures: brent oil, corn, gold, coal, natural gas, and sugar. Some of these were chosen because of the periods considered (when they became essential commodities), that is, before and after the 2022 Russia–Ukraine conflict. Agents behavior was assessed using a series of financial
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Discussion of specifying prior distributions in reliability applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-07-04 Simon Wilson
This is a thorough review of approaches to prior elicitation in reliability and includes some extensive illustrations of the approaches. For me, this article is both a very useful reference document and can act as a good primer for new students in the reliability field who would like to understand better how prior elicitation can be undertaken in reliability applications. The focus is largely on uninformative
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Discussion of “Specifying prior distributions in reliability applications”: Towards new formal rules for informative prior elicitation? Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-30 Nicolas Bousquet
The article by Tian et al. (Appl. Stoch. Models Bus. Ind. 2023) takes an interesting look at the use of non-informative priors adapted to several censoring processes, which are common in reliability. It proposes a continuum of modelling approaches that go as far as defining weakly informative priors to overcome the well-known shortcomings of frequentist approaches to problems involving highly censored
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A stochastic model for evaluating the peaks of commodities' returns Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-26 Roy Cerqueti, Raffaele Mattera, Alessandro Ramponi
This paper proposes a probabilistic model for the evaluation of the peak components of the return of a commodity. The ground of the study lies in the evidence that the spikes in the returns are due to the shocks occurring in the external environment. We follow an approach based on a particular class of point processes—the Spatial Mixed Poisson Processes—by exploiting an invariance property for such
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A Bayesian record linkage model incorporating relational data Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-26 Juan Sosa, Abel Rodríguez
In this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning
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Discussion of: Specifying prior distributions in reliability applications Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-20 Richard Arnold
This interesting paper by Tian et al. presents a comprehensive investigation of non-informative and weakly informative priors for two parameter (log-location and scale) failure distributions. They provide helpful and practical advice to the Bayesian analyst on the selection of appropriate priors and specifically on the avoidance of posterior estimates that are unrealistic, particularly where data are
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Deep partial least squares for instrumental variable regression Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-19 Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dimension reduction and feature selection from the set of instruments and covariates. A central theoretical result, due to Brillinger (2012) Selected Works
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Discussion of specifying prior distributions in reliability applications—Applications for Bayesian estimation software design Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-17 Peng Liu
It is a great pleasure to have the opportunity to write a discussion on “Specifying Prior Distributions in Reliability Applications” by Tian et al. Appl Stochast Models Bus Ind, (2023). One coauthor of the paper, Dr Meeker, has conducted Bayesian methodology research on reliability data analysis for many years, and I have followed his work on the subject for quite some time. The work by Dr Meeker helped
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On relevation transform involved with statistical dependence between two component lifetimes Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-14 Rui Fang, Chen Li, Xiaohu Li
Krakowski (Rev Fr Autom Inform Rech Opèr. 1973;7:107–120.) introduced the relevation transform for component and active redundancy with independent lifetimes, and except for Johnson and Kotz (Am J Math Manag Sci. 1981;1:155–165; Nav Res Logist. 1983;30:163–169.) most subsequent researches were conducted under this framework. However, it is not uncommon that a component and its active redundancy bear
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Discussion of “Specifying prior distributions in reliability applications” Appl. Stoch. Models Bus.Ind. (IF 1.4) Pub Date : 2023-06-13 Hon Keung Tony Ng
Specifying prior distributions in the Bayesian method is a fundamental but complex problem, especially when conjugate prior does not exist. Tian et al. (Appl Stoch Models Bus Ind; 2023) have captured the spirit of specifying prior distributions in Bayesian analysis for reliability data and presented different approaches coherently. In this discussion, I will focus on specifying prior distributions