Generalized information matrix tests for copulas

We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White and so relate to the copula test proposed by Huang and Prokhorov. The idea is that eigenspectrum-based statements of the IM equality reduce the degrees of freedom of the test’s asymptotic distribution and lead to better size-power properties, even in high dimensions. The gains are especially pronounced for vine copulas, where additional benefits come from simplifications of score functions and the Hessian. We derive the asymptotic distribution of the generalized tests, accounting for the nonparametric estimation of the marginals and apply a parametric bootstrap procedure, valid when asymptotic critical values are inaccurate. In Monte Carlo simulations, we study the behavior of the new tests, compare them with several Cramer–von Mises type tests and confirm the desired properties of the new tests in high dimensions.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.     

COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Risk Perception and Information Processing: The Development and Validation of a Questionnaire to Assess Self‐Reported Information Processing

The role of information processing in understanding people's responses to risk information has recently received substantial attention. One limitation of this research concerns the unavailability of a validated questionnaire of information processing. This article presents two studies in which we describe the development and validation of the Information‐Processing Questionnaire to meet that need. Study 1 describes the development and initial validation of the questionnaire. Participants were randomized to either a systematic processing or a heuristic processing condition after which they completed a manipulation check and the initial 15‐item questionnaire and again two weeks later. The questionnaire was subjected to factor reliability and validity analyses on both measurement times for purposes of cross‐validation of the results. A two‐factor solution was observed representing a systematic processing and a heuristic processing subscale. The resulting scale showed good reliability and validity, with the systematic condition scoring significantly higher on the systematic subscale and the heuristic processing condition significantly higher on the heuristic subscale. Study 2 sought to further validate the questionnaire in a field study. Results of the second study corresponded with those of Study 1 and provided further evidence of the validity of the Information‐Processing Questionnaire. The availability of this information‐processing scale will be a valuable asset for future research and may provide researchers with new research opportunities.     

The effect of cognitive diversity on the illusion of control bias in strategic decisions: An experimental investigation

By integrating cognitive diversity into debiasing literature, this paper contributes towards opening the black box of executive judgment. Based on information processing theory we investigate the role of cognitive diversity in strategic decision making. We apply a vignette-based experimental research design to examine the effect of cognitive diversity in teams on decision maker's illusion of control. The results of these experiments provide evidence for a positive influence of high cognitive diversity for debiasing judgment while similarly indicating no such effect for groups with low cognitive diversity. These findings suggest that group composition aspects can play an important role for improving judgment in decision making teams and open promising new avenues for studying debiasing in behavioral strategy research.     

Corporate statement serving as information subsidies in the product-harm crisis

This study explores the attributes of a crisis statement which influence its utility as an information subsidy in the process of product-harm crises in China. Through investigating and analyzing 92 statements released in times of product-harm crises, the results show that the form in which the statement is released, and the length of the statement have significant effects on whether the statement will be quoted fully or partially by the news media.     

Adolescents,Online Marketing and Privacy: Predicting Adolescents’ Willingness to Disclose Personal Information for Marketing Purposes

The present study aims at exploring which factors predict the willingness of adolescents to disclose personal information in response to online marketing requests. Our analyses show that especially privacy concerns and perceived benefits explain a considerable portion of variance in both willingness to disclose profile data (e.g. age, hobbies, favourite products) and contact data (e.g. phone number, e‐mail address, home address). An interesting interrelationship was found between profile data and contact data disclosure. Contrary to our expectations, parental mediation strategies were only a weak predictor of teenagers’ willingness to disclose contact information. Policy implications that emerge from the central findings of this study are discussed.     

An assessment of GRAI Grids and their use in the Strathclyde Integration Method

The purpose of this paper is to present a critical assessment of GRAI Grids as a tool for improved manufacturing integration. GRAI Grids, a key component of the GRAI approach, form an integral part of the Strathclyde Integration Method SIM for manufacturing integration. The contribution of the paper is thus to provide a 'third party' view of the effectiveness of GRAI Grids as a modelling tool. The paper presents a brief overview of the GRAI approach, concentrating particularly on the Grids, and an overview of SIM, showing how GRAI Grids are used. The main section of the paper presents an assessment and critique of GRAI Grids. Several benefits and limitations of the Grid as used in SIM and some suggestions for extensions are presented. The paper concludes that the use of the GRAI Grid, within a methodology, provides a unique insight into the behaviour and design of manufacturing information systems and that those who criticize the technique perhaps do so from a lack of awareness of its concepts, principles and advantages.     

Effect of Inventory Information Discrepancy in a Drop‐Shipping Supply Chain

This article investigates the impact of information discrepancy between a drop‐shipper and an online retailer on the drop‐shipping supply chain performance. The inventory information misalignment between them contributes to the failure of order fulfillment and demand satisfaction, and hence the associated penalties are incurred. In this article, we first analyze the penalties of ignoring such information discrepancy on both the drop‐shipper and the online retailer. We then assess the impact of information discrepancy on both parties when the drop‐shipper understands the existence of the information discrepancy but is not able to eliminate the errors. The numerical experiments indicate that both parties can have significant amount of the percentage cost reductions if the information discrepancy can be eliminated, and the potential savings are substantial especially when the errors have large variability. Furthermore, we observe that the online retailer is more vulnerable to information discrepancy than the drop‐shipper, and the drop‐shipper is likely to suffer from the online retailer's underestimation of the physical inventory level more than the problem of its overestimation. Moreover, even if eliminating errors is not possible, both parties could still benefit from taking the possibility of errors into consideration in decision making.     

环境信息披露的污染减排效应评估

本文利用我国2008年正式实施环境信息披露制度这一外生冲击构造准自然实验,基于2004-2017年我国285个城市的面板数据,通过双重差分法系统评估环境信息披露对工业污染物排放的影响。克服环境信息披露的测量困难与内生性问题,首次考察环境信息披露的污染减排效果,并借助数理模型对其影响机制进行规范阐释。研究发现,环境信息披露能够显著降低工业污染物排放水平,且该影响具有一定的时滞性和长期性;同时,减排作用会随地区环境污染程度和环境规制力度的增强而呈现递增规律;此外,机制分析的结果表明,其传导机制主要来自于产业结构转型和减排技术进步。为验证研究结论的稳健性,本文提供了平行趋势、工具变量、安慰剂等多种方法的检验结果。本文的研究在经验上丰富了环境信息披露与环境污染治理之间关系的相关讨论,为提升我国环境污染治理水平、打赢污染防治攻坚战提供有益的政策启示。