Linear functional regression: The case of fixed design and functional response

The authors consider the problem of simple linear regression when the exogenous and endogenous variables are functional and the design is fixed. They propose an estimator for the underlying linear operator and prove its consistency under some conditions which ensure that the design is sufficiently informative. They consider the classical calibration (or inverse regression) problem and analyze a consistent estimator. They also give a simulation study. The proposed method is not hard to implement in practice.     

Balanced allocation methods for claims problems with indivisibilities

We study claims problems in which a certain amount of indivisible units (of an homogeneous good) has to be distributed among a group of agents, when this amount is not enough to fully satisfy agents’ demands. Our aim is to find solutions fulfilling procedural and fairness properties. To do that, we define the M-down methods, which are the unique robust (composition down and consistency) and fair (balancedness and conditional full compensation) rules. Besides, we also establish the relationship between these M-down methods and the constrained equal awards rule.     

Forensic Evaluation in Alleged Sibling Incest Against Children

Sibling incest is a serious form of intrafamilial sexual abuse with health, social, and legal relevance. A retrospective study was conducted through the analysis of forensic medical reports of the alleged sibling incest of victims under 18 years old (n = 68) from 2004 to 2011 as well as the respective judicial outcomes. Results demonstrated that sibling’s sexual abuse is associated with several circumstances that might exacerbate its severity such as vaginal, anal, and/or oral penetration. Moreover, the victim’s young age, the proximity between victim and abuser, and the fact that it is committed at the victim’s and/or abuser’s home and by using physical violence and verbal threats justify a late detection of these cases.     

Technological innovation and operational effectiveness: their role in achieving performance improvements

The purpose of this article is to examine the role of the alignment between technological innovation effectiveness and operational effectiveness after the implementation of enterprise information systems, and the impact of this alignment on the improvement in operational performance. Confirmatory factor analysis was used to examine structural relationships between the set of observed variables and the set of continuous latent variables. The findings from this research suggest that the dimensions stemming from technological innovation effectiveness such as system quality, information quality, service quality, user satisfaction and the performance objectives stemming from operational effectiveness such as cost, quality, reliability, flexibility and speed are important and significantly well-correlated factors. These factors promote the alignment between technological innovation effectiveness and operational effectiveness and should be the focus for managers in achieving effective implementation of technological innovations. In addition, there is a significant and direct influence of this alignment on the improvement of operational performance. The principal limitation of this study is that the findings are based on investigation of small sample size.     

CAN WE INFER HOSPITAL QUALITY FROM MEDICAL GRADUATES’ RESIDENCY CHOICES?

This paper uses a novel approach to infer hospital technical quality from revealed preferences over residency programs. Specifically, we use Spanish medical graduates’ residency choices made from 1995 to 2000. We start by estimating a model of medical graduates preferences that controls for hospital, proximity, specialty, and gender effects. We interpret the coefficients on the hospital dummy variables as measures of medical graduates’ preferences over hospitals. Our results show that graduates do indeed discriminate between hospitals and that their preferences correlate with hospital‐specific covariates arguably related to hospital training quality. We then show that preferences from medical graduates are positively and statistically significantly correlated with risk‐adjusted hospital rankings based on five alternative outcome measures. Finally, we construct reputation scores for each hospital using news story counts in three media outlets and find that medical graduates’ preferences are especially valuable for inference of hospital technical quality of care as they do not simply reflect well known reputation.     

Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data

This article considers an approach to estimating and testing a new Kronecker product covariance structure for three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data. Testing of such covariance structure is potentially important for high dimensional multi-level multivariate data. The hypothesis testing procedure developed in this article can not only test the hypothesis for three-level multivariate data, but also can test many different hypotheses, such as blocked compound symmetry, for two-level multivariate data as special cases. The tests are implemented with two real data sets.     

Do Mediterraneans really do it better? Explaining the lower gender wage gap in southern European countries

This article sets out to help provide a more detailed explanation for the narrower gender wage gap in Mediterranean countries. Two explanatory hypotheses are put forward and compared empirically using the European Union Statistics on Income and Living Conditions (EU‐SILC). The most widely accepted hypothesis is that gender wage gaps across countries are negatively correlated with gender employment gaps. The second hypothesis, however, is supported by evidence that the narrower wage gap in Mediterranean countries could be due to the differences in demand for labour in EU countries as a result ultimately of decisions by major corporations concerning the location of the activities in their value chain.     

Continuous covariate frailty models for censored and truncated clustered data

Using some logarithmic and integral transformation we transform a continuous covariate frailty model into a polynomial regression model with a random effect. The responses of this mixed model can be ‘estimated’ via conditional hazard function estimation. The random error in this model does not have zero mean and its variance is not constant along the covariate and, consequently, these two quantities have to be estimated. Since the asymptotic expression for the bias is complicated, the two-large-bandwidth trick is proposed to estimate the bias. The proposed transformation is very useful for clustered incomplete data subject to left truncation and right censoring (and for complex clustered data in general). Indeed, in this case no standard software is available to fit the frailty model, whereas for the transformed model standard software for mixed models can be used for estimating the unknown parameters in the original frailty model. A small simulation study illustrates the good behavior of the proposed method. This method is applied to a bladder cancer data set.