Interracial rape: Examining the increasing frequency argument

A number of researchers have recently challenged the long-held notion among criminologists that rape, along with most other violent crimes, is an intraracial crime. Based on analysis of survey data they concluded that black rapists “prefer” or “seek out” white victims and that the likelihood of a black offender selecting a white victim has been increasing during recent years. Data presented here (1977–88 National Crime Surveys) refute the notion that black rapists “prefer” white victims. Moreover these same data fail to support the contention that rapes involving black offenders and white victims have been increasing. According to National Crime Survey estimates, the proportion of black offenders that victimize whites has been declining. An earlier version of this paper was presented at the annual meetings of the American Society of Criminology, November 1991.     

ON THE INDEPENDENCE OF THE SAMPLE MEAN AND TRANSLATION-INVARIANT STATISTICS FOR MATRIX NORMAL DISTRIBUTIONS

We present an explicit characterization of the joint dependency structure of an n×p matrix normal random matrix such that the p-dimensional sample mean vector is independent of all translation invariant statistics.     

Optimal Times to Contact Sample Households

Finding a suitable respondent at home is an essential and expensivecomponent of a household survey. This article reports on theresults of a study of the probabilities of finding someone aged14 or older at home and discusses the application of such datato survey design and budgeting.     

Digital Finance and FinTech: current research and future research directions

Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction—delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.     

An evaluation of the tandem configuration automated guided vehicle system

The performance of a tandem automated guided vehicle (AGV) system is compared with that of conventional AGV track systems. In the tandem system the track is divided into non-overlapping, single-vehicle closed loops. Using simulation it is shown that, because of trips requiring delivery across loops, the tandem system has a higher expected travel time per load and thus a greater average time in system. When the loads are delivered within the original loop only, the tandem system has a lower average time in system than in conventional systems, although the difference is modest. Track layout appears to influence the average time in system much more than docs the dispatching rule invoked. The success of the tandem system is highly dependent on approximately even AGV utilization, which may be difficult to obtain in practice.     

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols

In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the compliance with the EMA guideline, we evaluated the model specifications in those clinical study protocols from development phases II and III submitted between 2015 and 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy. Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modeling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%), and the fallback strategy (18%) are given by less than half the study protocols. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type I error rate control at the intended level is not guaranteed.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.     

Children's Perspectives on Happiness and Subjective Well‐being in Preschool

Photo‐elicited interviews indicate that children hardly ever mention educators when asked about elements in preschool that make them feel happy. Happiness is found to occur in activities in the ‘underlife’ of the ECEC institution. Children challenge adult rules and norms in order to create status in the peer‐group, while at the same time, they seek to construct social identity and maintain a positive relationship with their educators. A child that manages to balance both adult expectations and what is needed to participate in the underlife among peers, experiences happiness and thus, is in a good state of well‐being.     

The Politics and Discourse of Migrant Return: The Role of UNHCR and IOM in the Governance of Return

This article proposes that there is added value in moving beyond isolated studies of return-related migration policies in order to consider both deportations and so-called assisted voluntary returns under the common heading of ‘state-induced returns’. Based on official documents and interviews with staff members of the United Nations High Commissioner for Refugees and the International Organisation for Migration, it argues that international actors working in the field of migrant return engage in a type of task-sharing that goes beyond functional complementariness. With regard to the return of rejected asylum seekers, for instance, they legitimise each other's engagement as well as the overarching return objectives of governments, and are, therefore, involved in norm-building regarding the acceptability of state-induced returns. In addition to setting certain minimum standards regarding states' treatment of their immigrant population, international actors assist states in upholding control over them. Rather than merely replacing state-led regulation, international actors thus support domestic governments in reaching their migration control objectives, and thereby contribute to a stabilisation of state sovereignty in the governance of migration.