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Generating multivariate Poisson random variables is essential in many applications, such as multi echelon supply chain systems, multi-item/multi-period pricing models, accident monitoring systems, etc. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix, and therefore are rarely used in management science. Instead, multivariate Poisson data are commonly approximated by either univariate Poisson or multivariate Normal data. However, these approximations are often not adequate in practice. In this paper, we propose a conceptually appealing correction for NORTA (NORmal To Anything) for generating multivariate Poisson data with a flexible correlation structure and rates. NORTA is based on simulating data from a multivariate Normal distribution and converting it into an arbitrary continuous distribution with a specific correlation matrix. We show that our method is both highly accurate and computationally efficient. We also show the managerial advantages of generating multivariate Poisson data over univariate Poisson or multivariate Normal data. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Empirical research of online auctions has grown dramatically in recent years. Studies using publicly available bid data from such websites as eBay.com have found many divergences of bidding behavior and auction outcomes compared with ordinary offline auctions and auction theory. Among the main differences between online and offline auctions are the former's longer duration, anonymity of bidders and sellers, and low barriers of entry. Ail of these factors lead to dynamics in the bid arrival and price process that change throughout the auction. In this work we examine the price process in a large and diverse set of eBay auctions, for both low- and high-valued items, in terres of item, auction, bidder, and seller characteristics. We propose a family of differential equation models that captures online auction dynamics. In particular, we show that a second-order linear differential equation well approximates the dynamics that occur in our diverse set of auctions. We also introduce a multiple-comparisons test for comparing dynamic models of auction subpopulations, which we use to compare subpopulations of auctions grouped by characteristics of the auction, item, seller, and bidders. We find that price dynamics change throughout the auction and are influenced mostly by factors that affect the level of uncertainty about the outcome (e.g., seller rating, item condition) and the level of competitiveness (e.g., early bidding, number of bids). We accomplish the modeling tasks within the framework of principal differential analysis and functional data models. [ABSTRACT FROM AUTHOR]

A useful discrete distribution (the Conway–Maxwell–Poisson distribution) is revived and its statistical and probabilistic properties are introduced and explored. This distribution is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli and geometric). It also leads to the generalization of distributions derived from these discrete distributions (i.e. the binomial and negative binomial distributions). We describe three methods for estimating the parameters of the Conway–Maxwell–Poisson distribution. The first is a fast simple weighted least squares method, which leads to estimates that are sufficiently accurate for practical purposes. The second method, using maximum likelihood, can be used to refine the initial estimates. This method requires iterations and is more computationally intensive. The third estimation method is Bayesian. Using the conjugate prior, the posterior density of the parameters of the Conway–Maxwell–Poisson distribution is easily computed. It is a flexible distribution that can account for overdispersion or underdispersion that is commonly encountered in count data. We also explore two sets of real world data demonstrating the flexibility and elegance of the Conway–Maxwell–Poisson distribution in fitting count data which do not seem to follow the Poisson distribution. [ABSTRACT FROM AUTHOR]

The reliability of consecutive type systems has been approached from various angles. A new method is presented for deriving exact expressions, for the generating functions and the reliabilities of various consecutive-type systems. This method, based on Feller's run theory, is easy to implement, and leads to both recursive and nonrecursive formulas for the reliability. The nonrecursive expression is especially advantageous for systems with numerous components. In comparison to the n (number of components) computations that the recursive formulas require, the nonrecursive formula only requires the computation of the roots of a polynomial of order k. The method is extended for computing generating functions and reliabilities of systems with multi-state comportents as well as systems with s-dependent components. Consecutive system, nonrecursive reliability, parrial fraction expansion, recurrence relation, system reliability. [ABSTRACT FROM AUTHOR]

Systems that include multiple decision roles are encountered in many fields. We focus on systems with several states or levels where the rules determining when and how to move between the levels are based on runs or scans. Our target is to evaluate the proportion of time spent at each level. This information is valuable since it is required for constructing various cost functions. In this paper we address two questions: How to incorporate the system of levels and decision rules into a probabilistic setting and which measures to use in order to evaluate the proportion of time spent at each level. We take a waiting-time approach, and use special features of runs and scans distributions in order m investigate long and short-term measures of the time spent at the different levels of a system. We introduce a short term per-cycle measure and compare it with the finite-time measure. We find that the two measures can differ significantly. [ABSTRACT FROM AUTHOR]

Examines the statistical detection of anthrax outbreaks by tracking over-the-counter medication sales. Description on the monitoring grocery data to detect bioterrorism attack; Use of information technology to improve clinical preparedness for bioterrorism; Evaluation on the detection system for anthrax footprint.

Run-related distributions are frequently encountered in sampling inspection in the application of stopping rules for different phases of sampling. In many cases the generating function of these distributions is known; nevertheless, it is too complicated to derive the pdf using the ordinary method, and only the expectation and variance are obtained. We present an approach, based on partial fraction expansion, to derive the exact pdf of several run-related variables from their generating function. Using efficient algorithms for finding roots of polynomials and simple recursive formulas, we complete the method presented by Feller. We derive and present exact probability functions of some run-related variables. An application to sampling inspection of attribute data in batches and for continuous production is suggested. [ABSTRACT FROM AUTHOR]

Discusses the analysis and display of hierarchical life-time data from multistage systems or processes where subjects/items go through a sequence of stages in a certain order. Introduction of an example of promotion data in a university; Description of the univariate display methods; Variation of follow-up plots; Use of multidimensional scaling.

Forecasting hierarchical or grouped time series using a reconciliation approach involves two steps: computing base forecasts and reconciling the forecasts. Base forecasts can be computed by popular time series forecasting methods such as exponential smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts the base forecasts to ensure they are coherent. However, using ETS or ARIMA for base forecasts can be computationally challenging when there are a large number of series to forecast, as each model must be numerically optimized for each series. We propose a linear model that avoids this computational problem and handles the forecasting and reconciliation in a single step. The proposed method is very flexible in incorporating external data. We illustrate our approach using a dataset on monthly Australian domestic tourism, as well as a simulated dataset. We compare our approach to reconciliation using ETS and ARIMA, and show that our approach is much faster while providing similar levels of forecast accuracy. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the current and future role of stochastic and statistical modelling in business and industry, empowerment of statisticians, and online service economy.

A letter to the editor is presented related to scientific reproducibility and gauge repeatability and reproducibility (GR&R) testing.

In response to growing recognition of the social impacts of new artificial intelligence (AI)-based technologies, major AI and machine learning (ML) conferences and journals now encourage or require papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI research, at times devolving into name calling and threats of "cancellation." We diagnose this conflict as one between "atomist" and "holist" ideologies. Among other things, atomists believe facts are and should be kept separate from values, while holists believe facts and values are and should be inextricable from one another. With the goal of reducing disciplinary polarization, we draw on numerous philosophical and historical sources to describe each ideology's core beliefs and assumptions. Finally, we call on atomists and holists within the ever-expanding data science community to exhibit greater empathy during ethical disagreements and propose four targeted strategies to ensure AI research benefits society., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction of the European Union's (EU's) General Data Protection Regulation (GDPR) in May 2018 will have far-reaching effects on data scientists and researchers who use BBD, not only in the EU, but around the world. Consequently, many companies are struggling to comply with the Regulation. At the same time, academics interested in research collaborations with companies are finding it more difficult to obtain data. In light of the importance of BBD in both industry and academia, data scientists and behavioral researchers would benefit from a deeper understanding of the GDPR's key concepts, definitions, and principles, especially as they apply to the data science workflow. We identify key GDPR concepts and principles and describe how they can impact the work of data scientists and researchers in this new data privacy regulation era.

Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of researchers in social science and management fields acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data. Behavioral researchers using BBD face not only methodological and technical challenges but also ethical and moral dilemmas. In this article, we discuss several dilemmas, challenges, and trade-offs related to acquiring and analyzing BBD for causal behavioral research.