Contributions of the National Incident-Based Reporting System to Substantive Knowledge in Criminology: A Review of Research

The National Incident-Based Reporting System (NIBRS) data have been available to the research community for about a decade. The advantages of NIBRS over other official statistics such as summary Uniform Crime Reports, Supplementary Homicide Reports, and the National Crime Victimization Survey have contributed to better evaluation of a number of theoretical perspectives in criminology and criminal justice, and have helped in suggesting possible policy implications. This paper reviews studies using the NIBRS data in the areas of crime clearance, crime rates disaggregated by situational context (including domestic violence, race-specific crime, and gun crime), and lethal and non-lethal outcomes of violent encounters. The paper also discusses limitations of the NIBRS data and cautions for its use, along with future research possibilities.     

Understanding predictive information criteria for Bayesian models

We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a bias-corrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this paper is to put all these information criteria into a Bayesian predictive context and to better understand, through small examples, how these methods can apply in practice.     

Innovation in family firms: an empirical analysis linking organizational and managerial innovation to corporate success

Family firms play a significant role in national economies worldwide, accounting e.g. for 85% of all enterprises in the OECD countries as well as for the majority of companies in Central Europe. Previous scholarly research on family firms has mostly focused on the question of how they differ from public corporations, describing family firms as being more conservative, less risk-raking, or reluctant to grow—in sum, as being less entrepreneurial than their non-family counterparts. Similarly, the existing literature often criticizes the lack of innovation in family firms. But since innovation has long been discovered as one of the key drivers to company success, it is surprising that its role in family firms has been mostly neglected in existing academic research so far. The aim of this article is therefore to study the role of (managerial and organizational) innovation in family firms compared to non-family firms on the basis of an empirical survey of 533 companies from Finland, using structural equation modelling (MPlus) for the statistical analyses.     

The Welfare Consequences of Strategic Voting in Two Commonly Used Parliamentary Agendas

This paper studies the welfare consequences of strategic voting in two commonly used parliamentary agendas by comparing the average utilities obtained in simulated voting under two behavioural assumptions: expected utility maximising behaviour and sincere behaviour. The average utility obtained in simulations is higher with expected utility maximising behaviour than with sincere voting behaviour under a broad range of assumptions. Strategic voting increases welfare particularly if the distribution of preference intensities correlates with voter types.      

Benzene Risk Estimation Using Radiation Equivalent Coefficients

We estimated benzene risk using a novel framework of risk assessment that employed the measurement of radiation dose equivalents to benzene metabolites and a PBPK model. The highest risks for 1 μg/m³ and 3.2 mg/m³ life time exposure of benzene estimated with a linear regression were 5.4 × 10⁻⁷ and 1.3 × 10⁻³, respectively. Even though these estimates were based on in vitro chromosome aberration test data, they were about one-sixth to one-fourteenth that from other studies and represent a fairly good estimate by using radiation equivalent coefficient as an "internal standard."     

Is more health always better for society? Exploring public preferences that violate monotonicity

There has recently been some literature on the properties of a Health-Related Social Welfare Function (HRSWF). The aim of this article is to contribute to the analysis of the different properties of a HRSWF, paying particular attention to the monotonicity principle. For monotonicity to be fulfilled, any increase in individual health—other things equal—should result in an increase in social welfare. We elicit public preferences concerning trade-offs between the total level of health (concern for efficiency) and its distribution (concern for equality), under different hypothetical scenarios through face-to-face interviews. Of key interests are: the distinction between non-monotonic preferences and Rawlsian preferences; symmetry of HRSWF; and the extent of inequality neutral preferences. The results indicate strong support for non-monotonic preferences, over Rawlsian preferences. Furthermore, the majority of those surveyed had preferences that were consistent with a symmetric and inequality averse HRSWF.     

Waiting time distributions for a run with additional constraints

This paper proposes a method for obtaining the exact probability of occurrence of the first success run of specified length with the additional constraint that at every trial until the occurrence of the first success run the number of successes up to the trial exceeds that of failures. For the sake of the additional constraint, the problem cannot be solved by the usual method of conditional probability generating functions. An idea of a kind of truncation is introduced and studied in order to solve the problem. Concrete methods for obtaining the probability in the cases of Bernoulli trials and time-homogeneous {0,1}$\{0,1\}$-valued Markov dependent trials are given. As an application of the results, a modification of the start-up demonstration test is studied. Numerical examples which illustrate the feasibility of the results are also given.     

A classifier under the strongly spiked eigenvalue model in high-dimension,low-sample-size context

Abstract

We consider the classification of high-dimensional data under the strongly spiked eigenvalue (SSE) model. We create a new classification procedure on the basis of the high-dimensional eigenstructure in high-dimension, low-sample-size context. We propose a distance-based classification procedure by using a data transformation. We also prove that our proposed classification procedure has consistency property for misclassification rates. We discuss performances of our classification procedure in simulations and real data analyses using microarray data sets.