Are there asymmetries in the effects of training on the conditional male wage distribution?

Recent studies have used quantile regression (QR) techniques to estimate the impact of education on the location, scale and shape of the conditional wage distribution. In our paper we investigate the degree to which work-related training – another important form of human capital – affects the location, scale and shape of the conditional wage distribution. Using the first six waves of the European Community Household Panel, we utilise both ordinary least squares and QR techniques to estimate associations between work-related training and wages for private sector men in ten European Union countries. Our results show that, for the majority of countries, there is a fairly uniform association between training and hourly wages across the conditional wage distribution. However, there are considerable differences across countries in mean associations between training and wages

Am I missing something? The effects of absence from class on student performance

We exploit a rich administrative panel data-set for cohorts of Economics students at a UK university in order to identify causal effects of class absence on student performance. We exploit the panel properties of the data to control for unobserved heterogeneity across students and hence for endogeneity between class absence and academic performance of students stemming from the likely influence of effort and ability on both absence and performance. Our estimations also exploit features of the data such as the random assignment of students to classes and information on the timetable of classes, which provides potential instruments in our identification strategy. Among other results we find, from a quantile regression specification, that there is a causal effect of absence on performance for students : missing class leads to poorer performance. There is evidence that this is particularly true for better-performing students, consistent with our hypothesis that effects of absence on performance are likely to vary with factors such as student ability.Randomised experiments ; quantile regression ; selection correction ; panel data ; education ; student performance ; class absence

Doctor who? Who gets admission offers in UK medical schools

In the context of the UK Government’s ambitious programme of medical school expansion, it is important to have an understanding of how the medical school admissions process works, and with what effects. The issue is also relevant for the Schwartz Review (2004) into higher education admissions. Using individual-level data for two entire cohorts of medical student applicants in UK universities and exploiting the panel structure of the applicant-medical school information, we estimate models to analyse the probability that an individual student receives an offer of a place. We find that prior qualifications, school type, gender, age, social class and ethnic background are major influences on whether a student receives an offer from a medical school. We also find that the probability of receiving an offer from a particular medical school is influenced by the identity of other medical schools applied to. Finally, we find evidence that certain groups of applicants are particularly disadvantaged the later they apply within the application process

A hazard model of the probability of medical school dropout in the United Kingdom

From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons

Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer

Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

The linked survival prospects of siblings : evidence for the Indian states

This paper reports an analysis of micro-data for India that shows a high correlation in infant mortality among siblings. In 13 of 15 states, we identify a causal effect of infant death on the risk of infant death of the subsequent sibling (a scarring effect), after controlling for mother-level heterogeneity. The scarring effects are large, the only other covariate with a similarly large effect being mother’s (secondary or higher) education. The two states in which evidence of scarring is weak are Punjab, the richest, and Kerala, the socially most progressive. The size of the scarring effect depends upon the sex of the previous child in three states, in a direction consistent with son-preference. Evidence of scarring implies that policies targeted at reducing infant mortality will have social multiplier effects by helping avoid the death of subsequent siblings. Comparison of other covariate effects across the states offers some interesting new insights

Decreased Fat Storage by Lactobacillus Paracasei Is Associated with Increased Levels of Angiopoietin-Like 4 Protein (ANGPTL4)

Background: Intervention strategies for obesity are global issues that require immediate attention. One approach is to exploit the growing consensus that beneficial gut microbiota could be of use in intervention regimes. Our objective was to determine the mechanism by which the probiotic bacteria Lactobacillus paracasei ssp paracasei F19 (F19) could alter fat storage. Angiopoietin-like 4 (ANGPTL4) is a circulating lipoprotein lipase (LPL) inhibitor that controls triglyceride deposition into adipocytes and has been reported to be regulated by gut microbes. Methodology/Principal Findings: A diet intervention study of mice fed high-fat chow supplemented with F19 was carried out to study potential mechanistic effects on fat storage. Mice given F19 displayed significantly less body fat, as assessed by magnetic resonance imaging, and a changed lipoprotein profile. Given that previous studies on fat storage have identified ANGPTL4 as an effector, we also investigated circulating levels of ANGPTL4, which proved to be higher in the F19-treated group. This increase, together with total body fat and triglyceride levels told a story of inhibited LPL action through ANGPTL4 leading to decreased fat storage. Co-culture experiments of colonic cell lines and F19 were set up in order to monitor any ensuing alterations in ANGPTL4 expression by qPCR. We observed that potentially secreted factors from F19 can induce ANGPTL4 gene expression, acting in part through the peroxisome proliferator activated receptors alpha and gamma. To prove validity of in vitro findings, germ-free mice were monocolonized with F19. Here we again found change