Non‐parametric Analysis of Gap Times for Multiple Event Data: An Overview

Multiple event data are frequently encountered in medical follow‐up, engineering and other applications when the multiple events are considered as the major outcomes. They may be repetitions of the same event (recurrent events) or may be events of different nature. Times between successive events (gap times) are often of direct interest in these applications. The stochastic‐ordering structure and within‐subject dependence of multiple events generate statistical challenges for analysing such data, including induced dependent censoring and non‐identifiability of marginal distributions. This paper provides an overview of a class of existing non‐parametric estimation methods for gap time distributions for various types of multiple event data, where sampling bias from induced dependent censoring is effectively adjusted. We discuss the statistical issues in gap time analysis, describe the estimation procedures and illustrate the methods with a comparative simulation study and a real application to an AIDS clinical trial. A comprehensive understanding of challenges and available methods for non‐parametric analysis can be useful because there is no existing standard approach to identifying an appropriate gap time method that can be used to address research question of interest. The methods discussed in this review would allow practitioners to effectively handle a variety of real‐world multiple event data.     

Portfolio selection based on semivariance and distance correlation under minimum variance framework

In the minimum variance model, the covariance matrix plays an important role because it measures the risk and relationship of asset returns simultaneously under the normality assumption. However, in practice, the distribution of asset returns is nonnormal and has an obvious fat‐tail nature. In addition, the risk is one‐sided. In this paper, the main objective is to propose a better tool to replace the covariance matrix. The covariance matrix can be decomposed into two parts: a diagonal variance matrix and a square matrix with its elements being the Pearson correlation coefficient. A substitution of the covariance matrix is presented by replacing the variance and Pearson correlation coefficient in the decomposition of the covariance matrix with a semivariance and distance correlation coefficient, respectively. The proposed portfolio optimization strategy is applied to empirical data, and the numerical studies show the strategy performs well.     

Second‐order analysis of anisotropic spatiotemporal point process data

Second‐order orientation methods provide a natural tool for the analysis of spatial point process data. In this paper, we extend to the spatiotemporal setting the spatial point pair orientation distribution function. The new space–time orientation distribution function is used to detect space–time anisotropic configurations. An edge‐corrected estimator is defined and illustrated through a simulation study. We apply the resulting estimator to data on the spatiotemporal distribution of fire ignition events caused by humans in a square area of 30 × 30 km² for 4 years. Our results confirm that our approach is able to detect directional components at distinct spatiotemporal scales. © 2014 The Authors. Statistica Neerlandica © 2014 VVS.     

Risky Businesses? Young People in Informal Self‐Employment in Sofia

This article studies how perceptions of the risks associated with informal self‐employment depend on the interplay between the institutional, structural (network) and cultural embeddedness of economic action. Informal self‐employment should create at least three types of risk. The first concerns the possible legal and social sanctions that stem from the illegal character of the entrepreneurial action. The second is related to the complete lack of social security protection among those for whom informal self‐employment is their sole employment. The third is connected with the lack of guarantees concerning contract enforcement, which may increase the probability of opportunistic behaviour by business partners and clients. On the basis of a qualitative study of young, highly educated, informally self‐employed workers in Bulgaria's capital Sofia, I argue that these risks are compensated by the specific network and cultural embeddedness of the economic action. This compensation takes the form of various types of insurance against risks. Its core is the replacement of the vacuum of institutional‐system trust with interpersonal trust. Thus, the specific constellation of institutional, network and cultural embeddedness is able to solve the problem of opportunism, as well as to create the perception that the informally self‐employed are faced with not much greater risks than registered self‐employed workers.     

Bayesian Analysis of a Sensitive Proportion for a Small Area

Without accounting for sensitive items in sample surveys, sampled units may not respond (nonignorable nonresponse) or they respond untruthfully. There are several survey designs that address this problem and we will review some of them. In our study, we have binary data from clusters within small areas, obtained from a version of the unrelated‐question design, and the sensitive proportion is of interest for each area. A hierarchical Bayesian model is used to capture the variation in the observed binomial counts from the clusters within the small areas and to estimate the sensitive proportions for all areas. Both our example on college cheating and a simulation study show significant reductions in the posterior standard deviations of the sensitive proportions under the small‐area model as compared with an analogous individual‐area model. The simulation study also demonstrates that the estimates under the small‐area model are closer to the truth than for the corresponding estimates under the individual‐area model. Finally, for small areas, we discuss many extensions to accommodate covariates, finite population sampling, multiple sensitive items and optional designs.     

How do corporate environmental policy and corporate reputation affect risk‐adjusted financial performance?

The current study aims to answer dual, related questions: Does corporate environmental policy affect corporate reputation, and does this link also influence risk‐adjusted profitability and company's risk? With a comprehensive framework involving analyses of each question, among a sample of firms traced by the Reputation Institute, this study reveals several notable results, after correcting for endogeneity biases. First, environmental engagement and green product innovation are both antecedents of corporate reputation. Second, corporate reputation has a positive impact on risk‐adjusted profitability and Z score indicator of financial distress risk. Thus, corporate environmental responsibility and green practices represent cospecialized assets that enhances an intangible asset, namely, corporate reputation. The latter influence constitutes a missing link between sustainable development and the firm's financial performance. Overall, environmental engagement and corporate reputation act as insurance‐like protections of firm competitiveness.     

Bayesian interval estimation for the difference of two independent Poisson rates using data subject to under‐reporting

Comparing occurrence rates of events of interest in science, business, and medicine is an important topic. Because count data are often under‐reported, we desire to account for this error in the response when constructing interval estimators. In this article, we derive a Bayesian interval for the difference of two Poisson rates when counts are potentially under‐reported. The under‐reporting causes a lack of identifiability. Here, we use informative priors to construct a credible interval for the difference of two Poisson rate parameters with under‐reported data. We demonstrate the efficacy of our new interval estimates using a real data example. We also investigate the performance of our newly derived Bayesian approach via simulation and examine the impact of various informative priors on the new interval.     

On Quantile‐based Asymmetric Family of Distributions: Properties and Inference

In this paper, we provide a detailed study of a general family of asymmetric densities. In the general framework, we establish expressions for important characteristics of the distributions and discuss estimation of the parameters via method‐of‐moments as well as maximum likelihood estimation. Asymptotic normality results for the estimators are provided. The results under the general framework are then applied to some specific examples of asymmetric densities. The use of the asymmetric densities is illustrated in a real‐data analysis.     

Extreme Value Theory and Statistics of Univariate Extremes: A Review

Statistical issues arising in modelling univariate extremes of a random sample have been successfully used in the most diverse fields, such as biometrics, finance, insurance and risk theory. Statistics of univariate extremes (SUE), the subject to be dealt with in this review paper, has recently faced a huge development, partially because rare events can have catastrophic consequences for human activities, through their impact on the natural and constructed environments. In the last decades, there has been a shift from the area of parametric SUE, based on probabilistic asymptotic results in extreme value theory, towards semi‐parametric approaches. After a brief reference to Gumbel's block methodology and more recent improvements in the parametric framework, we present an overview of the developments on the estimation of parameters of extreme events and on the testing of extreme value conditions under a semi‐parametric framework. We further discuss a few challenging topics in the area of SUE. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Do Powerful CEOs Determine Microfinance Performance?

Recently, microfinance has been coming under public and media attacks. The microcredit crisis following from microfinance‐induced suicides in 2010 in the Indian state of Andhra Pradesh indicates that weak corporate governance and imprudent risk taking have far‐reaching consequences. Yet, analyses of corporate governance mechanisms among microfinance institutions (MFIs) remain underdeveloped. As a response, this study examines the impact of CEO power on MFI risk taking by deriving explicit predictions of this effect from a characterization of the microfinance industry. Based on a sample of 280 microfinance institutions, our results suggest that powerful CEOs of microfinance non‐governmental organizations (NGOs) have more decision‐making freedom than powerful CEOs of other types of MFIs. This induces them to make more extreme decisions that increase risk. Furthermore, the decision‐making freedom powerful CEOs have in NGOs appears to lead to worse decisions, because the presence of powerful CEOs in microfinance NGOs is associated with lower performance.     

CHOOSING AMONG ALTERNATIVE LONG‐RUN EVENT‐STUDY TECHNIQUES

This paper reviews the long‐run event‐study debate by outlining the strengths and weakness of the most commonly used alternative techniques. The fist part of the discussion highlights that prior literature has failed to provide a single risk‐adjusted model of long‐run abnormal returns with no biases. Subsequently, the paper provides guidance on how one can choose among pertinent alternative techniques. As a conclusion, researchers ought to choose among alternative techniques after considering issues such as (i) the nature of dataset and market of interest, (ii) the event type (regulatory or corporate), (iii) returns’ time‐interval, (iv) association of the event with accounting data, (v) sample characteristics and prior evidence regarding similar events, as well as (vi) risk changes following the event. Robustness tests are essential, while the road for further research regarding the appropriate technique(s) is open.     

Stratified studies about risk ratios in 2 × 2 tables with structural zero

Incomplete correlated 2 × 2 tables are common in some infectious disease studies and two‐step treatment studies in which one of the comparative measures of interest is the risk ratio (RR). This paper investigates the two‐stage tests of whether K RRs are homogeneous and whether the common RR equals a freewill constant. On the assumption that K RRs are equal, this paper proposes four asymptotic test statistics: the Wald‐type, the logarithmic‐transformation‐based, the score‐type and the likelihood ratio statistics to test whether the common RR equals a prespecified value. Sample size formulae based on hypothesis testing method and confidence interval method are proposed in the second stage of test. Simulation results show that sample sizes based on the score‐type test and the logarithmic‐transformation‐based test are more accurate to achieve the predesigned power than those based on the Wald‐type test. The score‐type test performs best of the four tests in terms of type I error rate. A real example is used to illustrate the proposed methods.     

Cause‐related events: fulfilling the objectives of social partnerships

• Cause‐related events are growing in frequency and popularity. These events enable corporates and not‐for‐profit organisations to collaborate for mutual benefit, within the strategic framework of a social partnership. However, while anecdotal evidence indicates that millions of dollars are invested in events, less is known about how the strategic objectives of social partnerships are achieved via cause‐related events. We present the findings of an ethnographic study of two social partnerships and contribute insights into how and why events help them achieve their strategic objectives. Case analysis data reveals that the fit between events and partnerships; the people, teams, and relationships; and collaboration of resources all contribute to generating competitive advantage and value. We discuss the managerial implications for those collaborating to organise a cause‐related event.

Copyright © 2016 John Wiley & Sons, Ltd.     

Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but nonindependent error terms

In this paper, we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving‐average models under the assumption that the errors are uncorrelated but not necessarily independent. We relax the standard independence assumption on the error terms in order to extend the range of applications of the seasonal autoregressive moving‐average models. We study the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations under weak assumptions on noise. We establish the asymptotic behavior of the proposed statistics. A set of Monte Carlo experiments and an application to monthly mean total sunspot number are presented.     

A project portfolio risk‐opportunity identification framework

This article introduces a framework to identify risks and opportunities during portfolio risk management that helps to decrease the uncertainty of achieving the strategic goals of the organization. The final output of the framework is a portfolio risk‐opportunity register, which highlights the potential events that could impact the achievement of the goals. An illustrative example of how risk‐opportunity identification can be conducted within this framework is also exposed. In spite of being theoretical in nature, the model contributes to the risk management domain applied specifically to project portfolio management, opening the possibility of further research for its verification.     

Motivating Employees to Work Beyond Retirement: A Multi‐Level Study of the Role of I‐Deals and Unit Climate

The present study investigates what role I‐deals (i.e. the idiosyncratic deals made between employees and their organization) play in the motivation of employees to continue working after retirement. We hypothesized two types of I‐deals (i.e. development and flexibility I‐deals) to be positively related to motivation to continue working. More specifically, we drew from continuity and personality theory to argue that the motivation to continue working is enhanced by I‐deals, because they fulfil people's needs for personalized work arrangements. Moreover, drawing from activity and disengagement theory it was hypothesized that two types of unit climate (i.e. accommodative and development climates) would moderate these relationships. Specifically, it was predicted that I‐deals would be positively related to motivation to continue working under conditions of low accommodative or high development climate. Results of a multi‐level study among 1083 employees in 24 units largely supported the above expectations; flexibility I‐deals related positively to motivation to continue working, and unit climate moderated the relation between development I‐deals and motivation to continue working.     

Local Powers of Optimal One‐sample and Multi‐sample Tests for the Concentration of Fisher‐von Mises‐Langevin Distributions

One‐sample and multi‐sample tests on the concentration parameter of Fisher‐von Mises‐Langevin distributions on (hyper‐)spheres have been well studied in the literature. However, only little is known about their behaviour under local alternatives, which is due to complications inherent to the curved nature of the parameter space. The aim of the present paper therefore consists in filling that gap by having recourse to the Le Cam methodology, which has recently been adapted from the linear to the spherical setup. We obtain explicit expressions of the powers for the most efficient one‐ and multi‐sample tests. As a nice by‐product, we are also able to write down the powers (against local Fisher‐von Mises‐Langevin alternatives) of the celebrated Rayleigh test of uniformity. A Monte Carlo simulation study confirms our theoretical findings and shows the empirical powers of the above‐mentioned procedures.     

Concentration behavior of the penalized least squares estimator

Consider the standard nonparametric regression model and take as estimator the penalized least squares function. In this article, we study the trade‐off between closeness to the true function and complexity penalization of the estimator, where complexity is described by a seminorm on a class of functions. First, we present an exponential concentration inequality revealing the concentration behavior of the trade‐off of the penalized least squares estimator around a nonrandom quantity, where such quantity depends on the problem under consideration. Then, under some conditions and for the proper choice of the tuning parameter, we obtain bounds for this nonrandom quantity. We illustrate our results with some examples that include the smoothing splines estimator.     

Addressing Risks in Green Retrofit Projects: The Case of Singapore

The objectives of this study are to identify the risks in green retrofit projects in Singapore; analyze their risk criticalities; compare the risk criticalities between conventional and green retrofit projects; and provide mitigation measures for the critical risks. Twenty risks and 37 mitigation measures were identified from a literature review. A questionnaire survey was performed with 30 professionals experienced in green retrofits, and five post‐survey interviews were conducted. The results indicated “post‐retrofit tenants’ cooperation risk” was the top risk, and that 19 risks were more critical in green retrofits than in conventional retrofits. Additionally, 28 mitigation measures obtained significant agreement.     

Comparison of three‐dimensional ROC surfaces for clustered and correlated markers,with a proteomics application

We propose a non‐parametric test to compare two correlated diagnostic tests for a three‐category classification problem. Our development was motivated by a proteomic study where the objectives are to detect glycan biomarkers for liver cancer and to compare the discrimination ability of various markers. Three distinct disease categories need to be identified from this analysis. We therefore chose to use three‐dimensional receiver operating characteristic (ROC) surfaces and volumes under the ROC surfaces to describe the overall accuracy for different biomarkers. Each marker in this study might include a cluster of similar individual markers and thus was considered as a hierarchically structured sample. Our proposed statistical test incorporated the within‐marker correlation as well as the between‐marker correlation. We derived asymptotic distributions for three‐dimensional ROC surfaces and subsequently implemented bootstrap methods to facilitate the inferences. Simulation and real‐data analysis were included to illustrate our methods. Our distribution‐free test may be simplified for paired and independent two‐sample comparisons as well. Previously, only parametric tests were known for clustered and correlated three‐category ROC analyses.