Sources of family violence

This paper suggests a direction for the exploration of the causes of family violence. Explanatory models of family violence were considered in this regard, with the recommendation that a multi-determined model should be considered to ensure the most accurate explanation. We suggest that family violence will be best understood and prevented or alleviated, if a model is used that considers the interaction of structural violence and the personality features of all the family members.     

The Mean is within One Standard Deviation of Any Median

Statistics education is often restricted to teaching the mathematical equations and theories that form the foundation of statistical analysis. It can be argued, however, that the interpretation of the analysis and the communication of the results are equally important. The interdisciplinary role of statisticians requires us to examine real-life problems critically and communicate analytical results to nonstatisticians in a clear and concise manner. This article discusses the importance of including writing assignments as a routine part of statistics courses and presents benefits of the increased use of writing.     

Uncertain Benefits Estimates for Reductions in Fine Particle Concentrations

The Environmental Protection Agency's (EPA's) estimates of the benefits of improved air quality, especially from reduced mortality associated with reductions in fine particle concentrations, constitute the largest category of benefits from all federal regulation over the last decade. EPA develops such estimates, however, using an approach little changed since a 2002 report by the National Research Council (NRC), which was critical of EPA's methods and recommended a more comprehensive uncertainty analysis incorporating probability distributions for major sources of uncertainty. Consistent with the NRC's 2002 recommendations, we explore alternative assumptions and probability distributions for the major variables used to calculate the value of mortality benefits. For metropolitan Philadelphia, we show that uncertainty in air quality improvements and in baseline mortality have only modest effects on the distribution of estimated benefits. We analyze the effects of alternative assumptions regarding the value of reducing mortality risk, whether the toxicity is above or below the average for fine particles, and whether there is a threshold in the concentration‐response relationship, and show these assumptions all have large effects on the distribution of benefits.     

Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

Novel quadratic programming approach for time series clustering with biomedical application

Fundamental problems in data mining mainly involve discrete decisions based on numerical analyses of data (e.g., class assignment, feature selection, data categorization, identifying outlier samples). These decision-making problems in data mining are combinatorial in nature and can naturally be formulated as discrete optimization problems. One of the most widely studied problems in data mining is clustering. In this paper, we propose a new optimization model for hierarchical clustering based on quadratic programming and later show that this model is compact and scalable. Application of this clustering technique in epilepsy, the second most common brain disorder, is a case point in this study. In our empirical study, we will apply the proposed clustering technique to treatment problems in epilepsy through the brain dynamics analysis of electroencephalogram (EEG) recordings. This study is a proof of concept of our hypothesis that epileptic brains tend to be more synchronized (clustered) during the period before a seizure than a normal period. The results of this study suggest that data mining research might be able to revolutionize current diagnosis and treatment of epilepsy as well as give a greater understanding of brain functions (and other complex systems) from a system perspective. This work was partially supported by the NSF grant CCF 0546574 and Rutgers Research Council grant-202018.