Several journal articles, websites, and conferences have recently focused on the emerging discipline of statistical engineering. This discipline focuses on developing the theory and practice of how to address large, complex, unstructured problems, particularly those that require data and analysis. While good statisticians and engineers have always applied statistical engineering, they did not generally

In this paper, particular shock models are studied for the case when the times between successive shocks and the magnitudes of shocks have discrete phase‐type distributions. The well‐known shock models such as delta shock model, extreme shock model, and the mixed shock model which is obtained by combining delta and extreme shock models are considered. The probability generating function and recursive

We draw attention to a problem that is often overlooked or ignored by companies practicing hypothesis testing (A/B testing) in online environments. We show that conducting experiments on limited inventory that is shared between variants in the experiment can lead to high false‐positive rates since the core assumption of independence between the groups is violated. We provide a detailed analysis of

In this note, some point of views on virtual ages are presented in terms of the discussion paper written by Finkelstein and Cha, which include generalized stochastic order‐based virtual ages, system‐level virtual ages, virtual ages in Weibull distribution and repair degrees with virtual ages. Finally, some possible future researches on virtual ages are described.

We connect copula properties with stochastic comparisons between order statistics (k‐out‐of‐n systems) and coherent systems with dependent components. The copula dependence properties lead to marginal distribution‐free ordering properties between systems. Conversely, ordering properties of systems lead to properties (or new proofs of properties) for the baseline copulas. These relationships can be

The trend of digitalization uses modern relationship models across global supply chain (SC) management. The main objective of this article is to identify SC current problems with the help of the knowledge sharing aspect using blockchain technology. This article addresses selected problems for managing modern SCs with a view to shared knowledge based on information management, effects of product and

A multivariate monitoring procedure is presented to detect changes in the parameter vector of the dynamic conditional correlation model. The procedure can be used to detect changes in both the conditional and unconditional variances as well as in the correlation structure of the model. The detector is based on the contributions of individual observations to the gradient of the quasi‐log‐likelihood

Crowdfunding has emerged as a major way to raise financial supports through collaborative contributions of the general public. In recent studies, promotional activities are found to be highly influential to the final outcome of crowdfunding projects. In order to conduct effective and efficient promotional campaigns, it is essential to contiguously monitor their progress status and investigate the determinants

This article focuses on the estimation of dispersion effects in off‐line quality control techniques. In this context, the Taguchi design for the optimal choice of process parameters is one of the most commonly used statistical methods. Starting from Taguchi methodology, we consider that an additive or a multiplicative model defines the relationship between the deterministic component and the variability

In practice, it is often important to know how a system is performing as compared with initial, prior assessment of its reliability characteristics (black‐box scenario). For degrading systems, this comparison can be executed by observing degradation dynamically and then comparing the corresponding remaining lifetime of an operating system at each instant of time with that of the black‐box scenario

In this article, aimed at the future “let go” electricity market, smart contracts for grid enterprises doing electricity transactions and charge settlements based on blockchain technology, as well as the trading model using the smart contracts, are proposed. Then the key technological difficulties are analyzed, and the solutions are given. The main goal of our research is to help developing the infrastructure

Several settings, where the notion of virtual age is employed, are discussed. We argue that the age reduction imperfect repair modeling is often not sufficiently justified in practice, as it is not possible to execute repair in most of real situations that conforms with this model. On the other hand, a shock‐based virtual age model is suggested and justified via the probabilities of failures on shocks

In many applications, survey data are collected from different survey centers in different regions. It happens that in some circumstances, response variables are completely observed while the covariates have missing values. In this article, we propose a joint spatial regression model for the response variable and missing covariates via a sequence of one‐dimensional conditional spatial regression models

Shannon entropy is a useful criterion for measuring the uncertainty (predictability) of lifetimes of engineering systems. In this work, we provide an explicit expression for the entropy of the residual lifetime of a working used system with exactly i failed components at time t, using dynamic signature. We also present additional results on bounds and ordering properties for the proposed entropy. We

The preservation of stochastic orders by distortion functions has become a topic of increasing interest in the reliability analysis of coherent systems. The reason of this interest is that the reliability function of a coherent system with identically distributed components can be represented as a distortion function of the common reliability function of the components. In this framework, we study

The generalized aggregation ∑ i = 1 n W i ϕ ( X i , a i ) arises in many research fields including applied probability, actuarial science, and reliability theory, where ϕ is a bivariate kernel function and a is a parameter vector. One of its remarkable features is that both X and W are dependent in many practical situations. Therefore, studying the stochastic properties of generalized aggregations

Planning a cost‐efficient monitoring policy of stochastic processes arises from many industrial problems. We formulate a simple discrete‐time monitoring problem of continuous‐time stochastic processes with its applications to several industrial problems. A key in our model is a doubling trick of the variables, with which we can construct an algorithm to solve the problem. The cost‐efficient monitoring

In the present paper we study a new exotic option offering participation in a dynamic asset allocation strategy, which is an extension of the well‐known Constant Proportion Portfolio Insurance (CPPI) strategy. Our novel approach consists in assuming that the percentage of wealth invested in stocks cannot go under a fixed level, called guaranteed minimum equity exposure (GMEE). In particular, our proposal

The ubiquitous internet is a multipurpose platform for finding information, an avenue for social interaction, and a primary customer touch‐point as a marketplace to conduct e‐commerce. The digital footprints of browsers are a rich source of data to drive sales. We use clickstreams (clicks) to track the evolution of session‐level customer browsing for modeling. We apply Markov chains (MC) to calculate

Statistical surveillance is a noteworthy endeavor in many health‐care areas such as epidemiology, hospital quality, infection control, and patient safety. For monitoring hospital adverse events, the Shewhart u ‐control chart is the most used methodology. One possible issue of the u ‐chart is that in health‐care applications the lower control limit (LCL) is often conventionally set to zero as the adverse

Consider a finite population of N items, where item i has a probability p i to be defective. The goal is to identify all items by means of group testing. This is the generalized group testing problem (hereafter GGTP). In the case of p 1 = … = p N  = p , Yao and Hwang (1990) proved that the pairwise testing algorithm is the optimal nested algorithm, with respect to the expected number of tests, for

Consider a risk model in which X 1,…, X n are n potential losses from different risky assets at the terminal time, and θ 1 , … , θ n are n discount factors over the period. In this paper, we establish some asymptotic formulas for the value at risk and conditional tail expectation of the total discounted loss S n = ∑ i = 1 n θ i X i of an investment portfolio. We also demonstrate our obtained results

Analysis of network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated historical data unchanging over time. However, most networks result from temporally evolving systems that exhibit intrinsic dynamic behavior. Monitoring such temporally varying networks to detect anomalous changes

The Birnbaum‐Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression

A data fusion method for the estimation of residential radon level distribution in any Pennsylvania county is proposed. The method is based on a multisample density ratio model with variable tilts and is applied to combined radon data from a reference county of interest and its neighboring counties. Beaver county and its four immediate neighbors are taken as a case in point. The distribution of radon

In this article, a comparison between the most promising nonparametric tests in a two‐sample stratified design for practical uses is performed. We compared methods that exhibit good small‐sample properties in order to be used with the most common stratum sizes. From the literature we identified as promising the following solutions: the aligned rank test, a small‐sample approximation for the ANOVA‐type

Nonparametric control charts have received increasing attention in process monitoring. In this article, a new nonparametric sign (SN) control chart with variable sample size (VSS) for a finite horizon process is developed. The novelty of this research lies in the incorporation of the VSS technique into the nonparametric SN chart for a finite horizon process, hence, resulting in the development of a

An resilience optimal evaluation of financial portfolios implies having plausible hypotheses about the multiple interconnections between the macroeconomic variables and the risk parameters. In this article, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call stress

We propose a data‐driven procedure, cross‐estimation for decision selection (CrEDS), to choose from an abundance of off‐the‐shelf statistical models or computer algorithms at a decision‐maker's disposal. CrEDS combines the ideas of cross‐validation (CV) and local smoothing, a nonparametric statistical technique. We demonstrate the power of CrEDS with five numerical experiments in inventory and revenue

Analysis of means (ANOM), similar to Shewhart control chart that exhibits individual mean effects on a graphical display, is an attractive alternative mean testing procedure for the analysis of variance (ANOVA). The procedure is primarily used to analyze experimental data from designs with only fixed effects. Recently introduced, the ANOM procedure based on the q‐distribution (ANOMQ procedure) generalizes

The literature on analytical applications in insurance tends to be either very general or rather technical, which may hold back the adoption of new important tools by industrial practitioners. Our goal is to stress that machine learning (ML) algorithms will play a significant role in the insurance industry in the near future and thus to encourage practitioners to learn and apply these techniques. After

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject‐domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear

The main characteristic of a binary test is the item response function (IRF) expressing the probability P (d, a) of an object under test (OUT), possessing ability a, to successfully overcome the test item (TI) of difficulty d. Each specific test requires its own definitions of TI difficulty and OUT ability and has its own P (d, a) describing the probability of “success” mentioned above. This is demonstrated

We distinguish the evaluation methods for two main kinds of investment strategies, namely, passive and active portfolio management. Passive portfolio management aims at tracking an underlying index as close as possible with the most important measure being the tracking error. To claim the tracking error not exceeding a certain threshold, we apply the concept of noninferiority test as opposed to the

This article defines and studies a stochastic process that combines two important stylized facts of financial data: reversion to the mean, and a flexible generalized stochastic volatility process: the 4/2 process. This work is motivated by the modeling of at least two financial asset classes: commodities and volatility indexes. We provide analytical expressions for the conditional characteristic functions

A new class of lifetime distributions, called tetration distribution, is presented based on the continuous iteration of the exponential‐minus‐one function. In particular, this distribution encompasses and extends the Weibull, Pareto, and Gompertz distributions. In addition, a characterization of the tail of the distribution is proposed through two indices, one of them encompassing the Pareto and Weibull

This article surveys recent development on Euclidean interpoint distances (IPDs). IPDs find applications in many scientific fields and are the building blocks of several multivariate techniques such as comparison of distributions, clustering, classification, and multidimensional scaling. In this article, we explore IPDs, discuss their properties and applications, and present their distributions for

Represented by the pioneering works of Professor Benjamin Kedem, zero crossings of time‐series data have been proven useful for characterizing oscillatory patterns in many applications such as speech recognition and brainwave analysis. Robustness against outliers and nonlinear distortions is one of the advantages of zero crossings in comparison with traditional spectral analysis techniques. This paper

A nonparametric method is developed to detect self‐similarity among the rescaled distributions of the log‐price variations over a number of time scales. The procedure allows to test the statistical significance of the scaling exponent that possibly characterizes each pair of time scales and to analyze the link between self‐similarity and liquidity, the core assumption of the fractal market hypothesis

In this article, stochastic differential equations are used to model the dynamics of a harvested population in the presence of weak Allee effects. Two optimal harvesting policies are presented, one with variable effort based on optimal control theory, which is for practical reasons inapplicable in a random environment, and the other with constant effort and easily applicable. For a logistic‐like model

Currently, large‐scale data for consumer goods, called scanner data, are obtained by scanning the bar codes of individual products at the points of sale of retail outlets. Many national statistical offices use scanner data to build consumer price statistics. In this process, as in other statistical procedures, the detection of abnormal transactions in sales prices is an important step in the analysis

The aim of this study is to consider the multivariate generalized hyperbolic (MGH) distribution for modeling financial log‐returns. Beginning with the multivariate geometric subordinated Brownian motion for asset prices, we first demonstrate that the mean‐variance mixing model of the multivariate normal law is natural for log‐returns of financial assets. This multivariate mean‐variance mixing model

For degradation data in reliability analysis, estimation of the first‐passage time (FPT) distribution to a threshold provides valuable information on reliability characteristics. Recently, Balakrishnan and Qin (2019; Applied Stochastic Models in Business and Industry, 35:571–590) studied a nonparametric method to approximate the FPT distribution of such degradation processes if the underlying process

The carbon emissions trading scheme combined with feed‐in tariff policy is viewed as a feasible policy mix to promote the energy system transferring to low or near‐ zero‐carbon emissions. To investigate the interaction between such policy mix and the diffusion of energy technologies, we establish a stochastic programming model to describe the technology choice between renewable and fossil energy technologies

In this article, we introduce three new nonparametric procedures for testing the equality of two lifetime distributions. The proposed testing processes are based on appropriately modified Wilcoxon‐type rank‐sum statistics. The exact null distribution of these statistics is studied and closed formulae for the corresponding exact probability of correct selection of the best population are derived for

In practice, quality characteristics do not always follow a normal distribution, and quality control processes sometimes generate non‐normal response outcomes, including continuous non‐normal data and discrete count data. Thus, achieving better results in such situations requires a new control chart derived from various types of response variables. This study proposes a procedure for monitoring response

To handle the ubiquitous problem of “dependence learning,” copulas are quickly becoming a pervasive tool across a wide range of data‐driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, healthcare, and many more. At the same time, despite their practical value, the empirical methods of “learning copula from data” have been unsystematic with

Deep Learning (DL) is combined with extreme value theory (EVT) to predict peak loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose a deep temporal extreme value model to capture these effects, which predicts the tail behavior of load spikes. Deep

Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on‐site design and surrogate modeling for a computer simulator acting on a high‐dimensional input space that, although relatively

This article proposes a dynamic Bayesian framework to analyze the leadership relationships between mutual funds. To this end, a two‐step procedure is proposed. First, a Bayesian rolling window based on the Capital Asset Pricing Model is used to estimate the evolution of mutual funds' market exposure over time. Then, a vector autoregressive (VAR) model is used to analyze the leader‐follower relationship

This article proposes the generalized discrete autoregressive moving‐average (GDARMA) model as a parsimonious and universally applicable approach for stationary univariate or multivariate time series. The GDARMA model can be applied to any type of quantitative time series. It allows to compute moment properties in a unique way, and it exhibits the autocorrelation structure of the traditional ARMA model

This paper proposes some Bayesian inferential procedures for the transformed Wiener (TW) process, a new degradation process that has been recently suggested in the literature to describe degradation phenomena where degradation increments are not necessarily positive and depend stochastically on the current degradation level. These procedures have been expressly conceived to allow one incorporating

Long waiting lists are a symbol of inefficiencies of hospital services. The dynamics of waiting lists are complex, especially when trying to understand how the lists grow due to the demand of a particular treatment relative to a hospital's capacity. Understanding the uncertainty of forecasting growth/decline of waiting lists could help hospital managers with capacity planning. We address this uncertainty

Joint progressive censoring schemes are quite useful to conduct comparative life‐testing experiment of different competing products. Recently, Mondal and Kundu (“A New Two Sample Type‐II Progressive Censoring Scheme,” Commun Stat‐Theory Methods; 2018) introduced a joint progressive censoring scheme on two samples known as the balanced joint progressive censoring (BJPC) scheme. Optimal planning of such

Modeling microstructures is an interesting problem not just in materials science, but also in mathematics and statistics. The most basic model for steel microstructure is the Poisson‐Voronoi diagram. It has mathematically attractive properties and it has been used in the approximation of single‐phase steel microstructures. The aim of this article is to develop methods that can be used to test whether

This article proposes a new approach to the conditional autoregressive range (CARR) model using the Birnbaum‐Saunders (BS) distribution. The model aims to develop volatility clustering, which incorporates extreme fluctuations, using a time‐varying evolution of the range process called the BSCARR model. Furthermore, diagnosis analysis tools for diagnosis analysis were developed to evaluate the goodness

Phase‐type distribution closure properties are utilized to devise algorithms for generating reliability functions of systems with basic structures. These structures include series, parallel, K‐out‐of‐N, and standby structures with perfect/imperfect switch. The algorithms form a method for system reliability modeling and analysis based on the relationship between the system lifetime and component lifetimes

Product cannibalization is a well‐known phenomenon in marketing, describing the case when a new product steals sales from another product under the same brand. A special case of cannibalization may occur when the older product reacts to the competitive strength of the newer one, absorbing the corresponding market shares. We show that such cannibalization occurred between two Apple products, the iPhone

To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, a novel procedure is proposed, which extends classical multivariate techniques to the monitoring of multivariate functional

This study proposes a model to optimize postsale services consisting of four factors: (a) the basic warranty length, (b) the extended warranty length, (c) the preventive maintenance level, and (d) the preventive maintenance interval. Furthermore, consumer demand for the product and extended warranty are considered as functions of the length of the basic warranty and extended warranty periods, respectively