Applied Stochastic Models Research Articles

BIBLIOMETRIC ANALYSIS ON BIG DATA APPLICATIONS IN INSURANCE SECTOR: PAST, PRESENT, AND FUTURE RESEARCH DIRECTIONS

In this study, the key areas and current trends in the field of big data applications in the insurance industry are identified, along with suggestions for future research initiatives. We identified the most prominent authors, journals, organizations, and countries based on their total publications and citations, showing their significance within the network, using bibliometric analysis on a sample of 191 articles retrieved from Scopus from 1976 to 2021. VOSviewer and R-Biblioshiny tools were used to generate the bibliometric output on these retrieved papers. The findings showed that although while a good number of writers from other parts of the world contributed to the literature on big data applications in the insurance industry, during this time, most research papers have listed the United States, India, and China as their affiliated countries. The yearly publication was either one or two, with some discontinuity, from 1976 to 2011, but since 2012, it has increased, exhibiting an exponential growth tendency. The three journals “Risks,” “Applied Stochastic Models in Business and Industry,” and “Expert Systems with Applications” are the most popular for including a sizable number of papers in the field of big data technologies in the insurance sector. Each of the top 10 authors in this field published two research papers during these 46 years. Seven areas, including fraud detection and prevention, risk assessment, pricing & rate making, technology utilization, risk management, claim processing & prediction, and finally digitalization, were the major focus of research papers on bigdata applications in the insurance business. The human-centered AI system development, adoption of wearable technology, personalization, and other topics were found to have received very little attention in this study. As a result, the researchers may now direct future research in this area. This study is completely new of its kind in the domain of insurance though few documents are available on the broad concept of finance.

Issue Information

Applied Stochastic Models in Business and IndustryVolume 39, Issue 3 p. 289-291 ISSUE INFORMATIONFree Access Issue Information First published: 13 June 2023 https://doi.org/10.1002/asmb.2693AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume39, Issue3May/June 2023Pages 289-291 RelatedInformation

Reply to Discussions of Multivariate dynamic modeling for Bayesian forecasting of business revenue

Applied Stochastic Models in Business and IndustryEarly View COMMENTARY Reply to Discussions of Multivariate dynamic modeling for Bayesian forecasting of business revenue Anna K. Yanchenko, Corresponding Author Anna K. Yanchenko [email protected] orcid.org/0000-0003-3891-9170 Duke University, Durham, North Carolina, USA Correspondence Anna K. Yanchenko, Duke University, Durham, NC, USA. Email: [email protected]Search for more papers by this authorGraham Tierney, Graham Tierney Duke University, Durham, North Carolina, USASearch for more papers by this authorJoseph Lawson, Joseph Lawson Duke University, Durham, North Carolina, USASearch for more papers by this authorChristoph Hellmayr, Christoph Hellmayr 84.51, Cincinnati, Ohio, USASearch for more papers by this authorAndrew Cron, Andrew Cron 84.51, Cincinnati, Ohio, USASearch for more papers by this authorMike West, Mike West Duke University, Durham, North Carolina, USASearch for more papers by this author Anna K. Yanchenko, Corresponding Author Anna K. Yanchenko [email protected] orcid.org/0000-0003-3891-9170 Duke University, Durham, North Carolina, USA Correspondence Anna K. Yanchenko, Duke University, Durham, NC, USA. Email: [email protected]Search for more papers by this authorGraham Tierney, Graham Tierney Duke University, Durham, North Carolina, USASearch for more papers by this authorJoseph Lawson, Joseph Lawson Duke University, Durham, North Carolina, USASearch for more papers by this authorChristoph Hellmayr, Christoph Hellmayr 84.51, Cincinnati, Ohio, USASearch for more papers by this authorAndrew Cron, Andrew Cron 84.51, Cincinnati, Ohio, USASearch for more papers by this authorMike West, Mike West Duke University, Durham, North Carolina, USASearch for more papers by this author First published: 08 April 2023 https://doi.org/10.1002/asmb.2760Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

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Applied Stochastic Models in Business and IndustryVolume 39, Issue 2 p. 157-159 ISSUE INFORMATIONFree Access Issue Information First published: 04 April 2023 https://doi.org/10.1002/asmb.2692AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume39, Issue2March/April 2023Pages 157-159 RelatedInformation

Discussion of Bayesian forecasting of business revenue

Applied Stochastic Models in Business and IndustryEarly View COMMENTARY Discussion of Bayesian forecasting of business revenue Chris Glynn, Corresponding Author Chris Glynn [email protected] Indeed Hiring Lab, Austin, TX, USA Correspondence Chris Glynn, Indeed Hiring Lab, USA. Email: [email protected]Search for more papers by this authorIda B. Johnsson, Ida B. Johnsson Chewy, Seattle, WA, USASearch for more papers by this authorMingzi Yi, Mingzi Yi Zillow, Seattle, WA, USASearch for more papers by this author Chris Glynn, Corresponding Author Chris Glynn [email protected] Indeed Hiring Lab, Austin, TX, USA Correspondence Chris Glynn, Indeed Hiring Lab, USA. Email: [email protected]Search for more papers by this authorIda B. Johnsson, Ida B. Johnsson Chewy, Seattle, WA, USASearch for more papers by this authorMingzi Yi, Mingzi Yi Zillow, Seattle, WA, USASearch for more papers by this author First published: 30 November 2022 https://doi.org/10.1002/asmb.2741Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Exchange Option Pricing Under Variance Gamma-Like Models

In this article, we focus on the pricing of exchange options when the risk-neutral dynamic of log-prices follows either the well-known variance gamma or the recent variance gamma++ process introduced in Gardini et al. (2022. “The Variance Gamma++ Process and Applications to Energy Markets.” Applied Stochastic Models in Business and Industry 38 (2): 391–418. https://doi.org/10.1002/asmb.v38.2.). In particular, for the former model we can derive a Margrabe's type formula whereas for the latter one we can write an ‘integral free’ formula. Furthermore, we show how to construct a general multidimensional versions of the variance gamma++ processes preserving both the mathematical and numerical tractabilities. Finally we apply the derived models to German and French energy power markets: we calibrate their parameters using real market data and we accordingly evaluate exchange options with the derived closed formulas, Fourier based methods and Monte Carlo techniques.

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Applied Stochastic Models in Business and IndustryVolume 38, Issue 6 p. 915-917 ISSUE INFORMATIONFree Access Issue Information First published: 05 December 2022 https://doi.org/10.1002/asmb.2631AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume38, Issue6November/December 2022Pages 915-917 RelatedInformation

Discussion of “Virtual age, is it real?”

The paper by Finkelstein and Cha centres around the attempt to model a system subject to failures after it has experienced a damage and/or failure episode. The paper discusses the validity of the assumptions of virtual age models, and provides helpful examples which elucidate the uses and shortcomings of models using a time transformation to mimic the effect of damage and ageing. We first recast their examples in a uniform notation. We make comments on the nature of empirical vs. physical models, and the advantages and disadvantages of these. We close with a brief note about our own shock/repair model and its relationship to the time transformed failure models discussed by Finkelstein and Cha.This is the peer reviewed version of the following article: Arnold, R., Chukova, S., & Hayakawa, Y. (2021). Discussion of “Virtual age, is it real?” Applied Stochastic Models in Business and Industry, 37(1), which has been published in final form at https://doi.org/10.1002/asmb.2585. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Improved techniques for parametric and nonparametric evaluations of the first‐passage time for degradation processes

AbstractFor 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 type is unknown. In this article, we propose some improved techniques based on saddlepoint approximation, which enhance those existing methods. Numerical examples and Monte Carlo simulation studies are used to illustrate the advantages of the proposed techniques. Limitations of the improved techniques are discussed and some possible solutions to such are proposed. Some concluding remarks and practical recommendations are provided based on the results.

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No abstract is available for this article.

Preface

Preface

Foreword: Special issue on statistics in quality and productivity

This special issue of Applied Stochastic Models in Business and Industry contains five papers that were presented at the 2017 Quality and Productivity Research Conference (QPRC) in Storrs, Connecticut (USA), hosted by the University of Connecticut. The QPRC is an annual conference (held since 1984) sponsored by the Quality and Productivity Section of the American Statistical Association (ASA). Its mission is to improve the quality of products and services and the efficiency of industries by stimulating the research and development of better statistical methods and to identify new application areas where statistics can have a significant impact. The 2019 QPRC will be held on June 10-13 in Washington, DC (USA), hosted by the American University (see https://www.american.edu/cas/qprc/). Additional information about QPRC, including links to websites of conferences held since 1999, can be found in the ASA Quality and Productivity community page: http://community.amstat.org/qp/events/qualityandproductivityresearchconference. The first paper of the issue, by Kevin Quinlan and Christine Anderson-Cook, discusses the problem of design of experiments for logistic regression models with the objective of evaluating performance of forensic algorithms. They show how a Bayesian approach is useful in this context and illustrate the application of their methodology using a forensic case study and several examples. The second paper, by Haim Bar, James Booth, Martin Wells, and Kangyan Liu, presents an approach to classification problems when the number of predictors is larger than the sample size. They show how the initial screening step is of value under the conditions of sparsity and compare performance of several classifiers in this setting. They illustrate the properties of the proposed approach using simulation and two case studies. The third paper, by Aleksey Polunchenko and Vasanthan Raghavan, compares the performance of the Cumulative Sum (CUSUM) chart and the Shiryaev-Roberts procedure for detecting changes in autocorrelated data. Although, asymptotically, both procedures possess a second-order optimality property for the selected comparison criterion, CUSUM was showing a somewhat better performance for all values of parameters used in the simulation study. The fourth paper, by Gabriel Odom, Kathryn Newhart, Tzahi Cath, and Amanda Hering, discusses the problem of multivariate statistical process monitoring when the data generating system can assume multiple states. They illustrate ways in which information about the current state of the monitored system can be used to improve problem detection and diagnosis. Finally, the fifth paper, by Muzaffer Musal and Tahir Ekin, presents an iterative sampling framework to estimate the proportion and number of overpaid claims and the overpayment recovery amount in a health care organization. The novel sampling resource allocation scheme proposed in the paper should be instrumental in the ongoing effort to reduce waste in health care systems and make them more efficient. We deeply appreciate the efforts of the QPRC-2017 Program Committee and Organizing Committee members, leading to this highly successful and well-attended event. We are also most thankful to its participants and sponsors for their generous investment of time and resources and to our editors and referees for their work on producing this special issue.

Foreword: Special issue on the future of applied stochastic models and data analysis

This special issue of the journal Applied Stochastic Models in Business and Industry consists in a choice of papers presented by participants to the 17th international conference on Applied Stochastic Models and Data Analysis, ASMDA 2017, held in London, June 6-9, 2017. Researchers, scientists, and practitioners from different parts of the world were brought together to present and discuss significant recent developments in stochastic modeling and data analysis. This issue includes eight research articles that were first presented at the conference, then selected by the guest editors, and finally undergone the peer-review process according to the standards of the journal. The first four papers deal with stochastic models with applications to finance and insurance. Ekaterina Bulinskaya proposes methods for asymptotic analysis and optimization solving in models arising in insurance and finance but not limited to these two only. Although only two simple cases are presented, the methods can be useful for solving other optimization and stability problems. Samuel Cohen, Robert Elliott, and Tak Kuen Siu give an introduction to risk neutral pricing of financial assets through a binomial model. Martingale representation, backward stochastic differential equations, and the Malliavin calculus, which are difficult concepts in a continuous time setting, are thus introduced via a standard simple discrete time model. Griselda Deelstra, Sinem Kozpinar, and Matthieu Simon consider the evaluation of spread and basket options when the underlying asset prices are driven by Markov-modulated Lévy processes with synchronous jumps. In this general regime-switching framework, lower and upper bounds to the exact option prices are obtained based upon ideas from the literature without regime switching. The obtained spread and basket option price approximations are illustrated with examples in different regime switching models. Claude Lefèvre and Philippe Picard consider a general epidemic model whose spread is partially controlled by a health organization. Thus, they show that standard life insurance principles can be applied to guarantee the insurance coverage of such an epidemic. The last four papers deal with classical themes on distributions for random variables, random vectors and random sequences, in probability, statistics and data analysis. Narayanaswamy Balakrishnan, Phalguni Nandab, and Suchandan Kayal deal with comparisons in terms of various stochastic orderings of the smallest- and largest-order statistics arising from independent heterogeneous generalized modified Weibull-distributed random variables. James R. Bozeman, Matt Davey, Sam Hutchins, Jillian Mori, Abigail Salvadore, and Kayla St. Germain propose a convexity ratio to serve as a guide to reshape voting districts that have likely been gerrymandered. Algorithms for finding the convexity ratio and designing nongerrymandered districts are proposed, applied to North Carolina and Illinois, and to reshape Texas voting districts. Comment are provided on how such a method can be used in other applications, for example, in geomarketing and on the electrical or police pistricting problem. Stéphanie Bougeard, Véronique Cariou, Gilbert Saporta, and Ndèye Niang deal with regression analysis from large data sets. A new method, called regularized clusterwise multiblock regression, is proposed, combining clustering and a multiblock component-based model associated with a well-defined criterion to optimize. A simulation study and a real example in the field of indoor air quality are presented, and an R-package, called mbclusterwise, is available. Jesus Garcia, Ramin Gholizadeh, and Veronica Gonzalez-Lopez develop a Bayesian information criterion to address the problem of deciding whether two independent samples coming from discrete Markovian processes are governed by the same stochastic law or not. A local metric between samples is thus defined for deriving the bound that must be used to take the decision. The distance is shown to be statistically sound and is applied to analyze the two lines of the production of alcohol fuel. We would like to thank all the authors for having submitted their articles and all the referees for their valuable work in the review process. We also thank the editor-in-chief of the journal ASMBI, Fabrizio Ruggeri, for his support and close implication in publishing this special issue.

Discussion of “Birnbaum‐Saunders distribution: A review of models, analysis, and applications” by N. Balakrishnan and Debasis Kundu

Applied Stochastic Models in Business and IndustryVolume 35, Issue 1 p. 61-63 DISCUSSION Discussion of “Birnbaum-Saunders distribution: A review of models, analysis, and applications” by N. Balakrishnan and Debasis Kundu Biswabrata Pradhan, Corresponding Author Biswabrata Pradhan bis@isical.ac.in SQC & OR Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata-700 108, India Biswabrata Pradhan, SQC & OR Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata-700 108, India. Email: bis@isical.ac.inSearch for more papers by this author Biswabrata Pradhan, Corresponding Author Biswabrata Pradhan bis@isical.ac.in SQC & OR Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata-700 108, India Biswabrata Pradhan, SQC & OR Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata-700 108, India. Email: bis@isical.ac.inSearch for more papers by this author First published: 17 September 2018 https://doi.org/10.1002/asmb.2407Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue1January/February 2019Pages 61-63 RelatedInformation

Discussion of the “Birnbaum‐Saunders distribution: A review of models, analysis, and applications”

Applied Stochastic Models in Business and IndustryVolume 35, Issue 1 p. 54-55 DISCUSSION Discussion of the “Birnbaum-Saunders distribution: A review of models, analysis, and applications” A. Wong, Corresponding Author A. Wong august@yorku.ca Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada A. Wong, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada. Email: august@yorku.caSearch for more papers by this author A. Wong, Corresponding Author A. Wong august@yorku.ca Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada A. Wong, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada. Email: august@yorku.caSearch for more papers by this author First published: 07 August 2018 https://doi.org/10.1002/asmb.2378Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue1January/February 2019Pages 54-55 RelatedInformation

Recent Developments in Copula Models

Copula models have become very popular and well studied among the scientific community.[...]

Special issue in honor of Kathryn Chaloner

We are honored to be guest editors for this special issue of Applied Stochastic Models in Business and Industry, dedicated to Kathryn Chaloner's life and achievements. Contributions from her colleagues and friends in this volume are devoted to Kathryn's early research interest in Bayesian optimal design. She was a pioneer in this field and had a broad impact on the subsequent literature on experimental design. Kathryn was internationally known for her research in Bayesian Statistics, which included design of experiments, outlier detection, prior elicitation, and clinical trials, and for her advancements in the study of HIV/AIDS. She was an accomplished researcher, teacher, and mentor. She actively encouraged women and under-represented minority students to join the field of biostatistics and provided mentorship, support, and encouragement. The special issue contains seven papers. The first three papers present some of the recent work in Bayesian optimal designs for nonlinear models, binary responses, and experiments with adversarial components. This is followed by two articles on Bayesian designs in clinical trials. The last two articles of the special issue involve engineering design problems. The paper by Giovagnoli considers the binary response models of Chaloner and Larntz (1989, Journal of Statistical Planning and Inference, 21:191–208) and develops an adaptive version of A-optimal Bayesian designs that are revised as additional data become available during the experiment. Adaptive Bayesian compound designs is also discussed as another extension. Konstantinou and Dette develop approximate Bayesian D-optimal designs for nonlinear regression models that involve covariates that cannot be observed directly. Applications to specific nonlinear models including the exponential regression model are considered and characterizations of the D-optimal saturated designs are presented. Optimal sample size selection for experiments is considered by De Santis and Gubbiotti in an adversarial setting where the decision makers have different prior opinions while having common utility functions and data. Using a Bayesian decision approach with a quadratic loss function, the authors present results for the one-parameter exponential family and investigate effect of the priors on the optimal sample size. Müller, Xu, and Thall discuss advantages and limitations of Bayesian decision theoretic approach in design of clinical trials. In doing so, the authors present a specific case study and point out the limitations such as the computational difficulties involved in solving sequential problems as well as selection of utility functions capturing different stakeholder interests. The article by Ventz, Parmigiani, and Trippa also consider limitations of the Bayesian framework in design of clinical trials and propose a strategy to alleviate these. The proposed strategy involves combining Bayesian designs with frequentist metrics such as error rates, confidence intervals, etc. that are commonly used by medical community and regulatory agencies. The authors show that this can be achieved by an inclusion of frequentist constraints into the Bayesian formulation. The constrained decision theoretic framework is illustrated via several applications and computational algorithms are presented for implementing the proposed approach. Nakamura, Seepaul, Kadane, and Reeja-Jayan discuss design of experiments in materials science. The authors consider use of a batch-sequential experiment and a regression model to determine optimal levels of input variables for synthesis of ceramic materials. It is illustrated that the optimal settings produce more reliable results than other experiments. The final article by Polson and Soyer considers Bayesian design of accelerated life tests for reliability assessment. An augmented probability simulation (APS) approach using Lindley's (1976, Annals of Statistics, 4:1–10) conjugate utility functions is proposed to compute optimal designs in an efficient manner. The proposed approach is illustrated using single and multiple point designs. Use of particle based methods for APS is also discussed as an alternative to Markov chain Monte Carlo methods.

第10回応用確率モデルとデータ解析国際シンポジウム(ASMDA 2001)に参加して(学術会合報告)

第10回応用確率モデルとデータ解析国際シンポジウム(ASMDA 2001)に参加して(学術会合報告)

Heterogeneity versus duration dependence with competing risks: an application to the labor market

Two hypotheses can explain the declining probability of gaining employment as an unemployment spell wears on: heterogeneity of the unemployed versus duration dependence. The nonparametric tests developed in the literature for testing duration dependence would not account for the fact that an unemployment spell can terminate in other ways than employment. The nonparametric tests developed in this paper extend, under certain conditions, those tests to competing risks. We illustrate our test using US unemployment data in which we find little consistent evidence for duration dependence. © 2017 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.

Panel‐based stratified cluster sampling and analysis for photovoltaic outdoor measurements

We study a stratified multisite cluster‐sampling panel time series approach in order to analyse and evaluate the quality and reliability of produced items, motivated by the problem to sample and analyse multisite outdoor measurements from photovoltaic systems. The specific stratified sampling in spatial clusters reduces sampling costs and allows for heterogeneity as well as for the analysis of spatial correlations due to defects and damages that tend to occur in clusters. The analysis is based on weighted least squares using data‐dependent weights. We show that this does not affect consistency and asymptotic normality of the least squares estimator under the proposed sampling design under general conditions. The estimation of the relevant variance–covariance matrices is discussed in detail for various models including nested designs and random effects. The strata corresponding to damages or manufacturers are modelled via a quality feature by means of a threshold approach. The analysis of outdoor electroluminescence images shows that spatial correlations and local clusters may arise in such photovoltaic data. Further, relevant statistics such as the mean pixel intensity cannot be assumed to follow a Gaussian law. We investigate the proposed inferential tools in detail by simulations in order to assess the influence of spatial cluster correlations and serial correlations on the test's size and power. ©2016 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.