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

The aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in the context of business and industry. We open with a discussion about the origin of Data Science and its requirement for a challenging mix of capability in data analytics, information technology, and business know‐how. The mission of Data Science is to provide new or revised computational theory able to extract

In this article, we propose a novel hybridization of regression trees (RTs) and radial basis function networks, namely, radial basis neural tree model, for waste recovery process (WRP) improvement in a paper industry. As a by‐product of the paper manufacturing process, a lot of waste along with valuable fibers and fillers come out from the paper machine. The WRP involves separating the unwanted materials

The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is

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

Bitcoins are traded on various exchange platforms and, therefore, prices may differ across trading venues. We aim to investigate return connectedness across eight of the major exchanges of Bitcoin, both from a static and a dynamic viewpoint. To this end, we employ an extension of the order‐invariant forecast error variance decomposition proposed by Diebold and Yilmaz (2012) to a generalized vector

Shipping companies are forced by the current EU regulation to set up a system for monitoring, reporting, and verification of harmful emissions from their fleet. In this regulatory background, data collected from onboard sensors can be utilized to assess the ship's operating conditions and quantify its CO2 emission levels. The standard approach for analyzing such data sets is based on summarizing the

This paper considers passive fund selection from an individual investor's perspective. The growth of the passive fund market over the past decade is staggering. Individual investors who wish to buy these funds for their retirement and brokerage accounts have many options and are faced with a difficult selection problem. Which funds do they invest in, and in what proportions? We develop a novel statistical

This article evaluates the impact of renewable energy consumption levels on GDP growth. Renewable energy sources may be significantly more costly as compared to classical (fossil) sources. Hence, high relative proportion of renewable energy has the potential of obstructing economic growth. On the other hand, in developed countries, renewable energy adoption processes are often subject to prominent

A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering

The Approximate Individual Sample Learning Entropy is based on incremental learning of a predictor , where x(k) is an input vector of a given size at time k, w is a vector of weights (adaptive parameters), and h is a prediction horizon. The basic assumption is that, after the underlying process x changes its behavior, the incrementally learning system will adapt the weights w to improve the predictor

Household consumption of natural gas is usually considered to be quite stable as cooking, space, and water heating belong to basic needs. The improvement of technologies together with possibilities of switching to alternative sources can, however, lead to a decreasing consumption trend. Knowing more about such trend, especially of its spatial distribution, can be useful for strategic planning. In this

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

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be

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

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

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

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

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

We propose a Bayesian framework to model bid placement time in retail secondary market online business‐to‐business auctions. In doing so, we propose a Bayesian beta regression model to predict the first bidder and time to first bid, and a dynamic probit model to analyze participation. In our development, we consider both auction‐specific and bidder‐specific explanatory variables. While we primarily

In the context of industrial engineering, cold‐standby redundancies allocation strategy is usually adopted to improve the reliability of coherent systems. This paper investigates optimal allocation strategies of cold standbys for series and parallel systems comprised of dependent components with left/right tail weakly stochastic arrangement increasing lifetimes. For the case of heterogeneous and independent

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

We investigate the company use of Twitter as a platform for social customer relations management (SCRM) to find that message type and follower growth follow an identifiable maturity model. Studying longitudinal Twitter data from 73 Standard and Poor's companies, we find that companies map into one of two distinct maturity stages as reflected in the content of company‐generated messages. The first maturity

We consider portfolio optimization in a regime‐switching market. The assets of the portfolio are modeled through a hidden Markov model (HMM) in discrete time, where drift and volatility of the single assets are allowed to switch between different states. We consider different parametrizations of the involved asset covariances: statewise uncorrelated assets (though linked through the common Markov chain)

In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described

This paper proposes a statistical method for identifying high‐density regions of pests, so‐called hot spots, within an orchard. Our method uses scanning windows to search for clusters of high counts within the sampled data. The proposed method enables a localized alternative for treatment that could be faster, less costly, and more environmentally friendly. R code that implements the hot spot identification

The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the

In this study, a mechanical system with linear deterioration and preventive maintenance is considered. The state of the system over time is represented by a semicontinuous stochastic process with dependent components. The system cycles through on and off periods during its lifetime. The state of the system deteriorates linearly as a function of the usage time during on periods. When the system is offline

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

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

Regularization methods, including Lasso, group Lasso and SCAD, typically focus on selecting variables with strong effects while ignoring weak signals. This may result in biased prediction, especially when weak signals outnumber strong signals. This paper aims to incorporate weak signals in variable selection, estimation and prediction. We propose a two-stage procedure, consisting of variable selection

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous-time methods for modeling such data are based on point processes and directly model interaction "contagion," whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable

In this article, we introduce a likelihood-based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions (Abanto-Valle et al., 2012). Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model

The intent of this discussion is to highlight opportunities and limitations of utility-based and decision theoretic arguments in clinical trial design. The discussion is based on a specific case study, but the arguments and principles remain valid in general. The example concerns the design of a randomized clinical trial to compare a gel sealant versus standard care for resolving air leaks after pulmonary

For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired

Dynamic response systems are often found in science, engineering, and medical applications, but the discussion on experimental design for such a system is relatively rare in literature. For an experimenter, designing such experiments requires making decisions on (1) when or where to take response measurements along the dynamic variable and (2) how to choose the combination of experimental factors and

Bayesian nonparametric (BNP) models provide a flexible tool in modeling many processes. One area that has not yet utilized BNP estimation is semi‐Markov processes (SMPs). SMPs require a significant amount of computation; this, coupled with the computation requirements for BNP models, has hampered any applications of SMPs using BNP estimation. This paper presents a modeling and computational approach

Changes in the behavior of dynamic systems are detected based on changes in the monitored quantities or their characteristics. This detection usually takes place by monitoring the time evolution of a variable and detecting the change at the time when a predetermined threshold is exceeded. This threshold is determined on the basis of the detection scheme requirements, in particular, the probability