(1975). The Limits of Organization. Journal of Economic Issues: Vol. 9, No. 3, pp. 543-544.

The Limits of Organization

Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding.

Foundations of Linear and Generalized Linear Models

Comparisons among countries can help to identify opportunities for the reduction of inequalities in health. We compared the magnitude of inequalities in mortality and self-assessed health among 22 countries in all parts of Europe.

Socioeconomic Inequalities in Health in 22 European Countries

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings.

Deep Over-sampling Framework for Classifying Imbalanced Data

Tropospheric ozone is an important greenhouse gas, is detrimental to human health and crop and ecosystem productivity, and controls the oxidizing capacity of the troposphere. Because of its high spatial and temporal variability and limited observations, quantifying net tropospheric ozone changes across the Northern Hemisphere on time scales of two decades had not been possible. Here, we show, using newly available observations from an extensive commercial aircraft monitoring network, that tropospheric ozone has increased above 11 regions of the Northern Hemisphere since the mid-1990s, consistent with the OMI/MLS satellite product. The net result of shifting anthropogenic ozone precursor emissions has led to an increase of ozone and its radiative forcing above all 11 study regions of the Northern Hemisphere, despite NO x emission reductions at midlatitudes.

https://advances.sciencemag.org/content/advances/6/34/eaba8272.full.pdf

Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere.

The paper aims to analyse the internal effectiveness of an university educational process by means of quantile regression. In particular, the goal is to evaluate how the students features affect the outcome of the University careers taking into account that this effect can be different for students with good or bad performances.

/pdf/the-evaluation-of-university-educational-processes-a-4aapmd4je8.pdf

The evaluation of University educational processes: a quantile regression approach

/pdf/quantile-regression-for-group-effect-analysis-4zd3kjolp2.pdf

Quantile regression for group effect analysis

A Statistical Approach to Neural Networks

Quantile composite-based path modeling is a recent extension to the conventional partial least squares path modeling. It estimates the effects that predictors exert on the whole conditional distributions of the outcomes involved in path models and provides a comprehensive view on the structure of the relationships among the variables. This method can also be used in a predictive way as it estimates model parameters for each quantile of interest and provides conditional quantile predictions for the manifest variables of the outcome blocks. Quantile composite-based path modeling is shown in action on real data concerning well-being indicators. Health outcomes are assessed taking into account the effects of Economic well-being and Education. In fact, to support an accurate evaluation of the regional performances, the conditions within the outcomes arise should be properly considered. Assessing health inequalities in this multidimensional perspective can highlight the unobserved heterogeneity and contribute to advances in knowledge about the dynamics producing the well-being outcomes at local level.

/pdf/composite-based-path-modeling-for-conditional-quantiles-4pmsxj70gw.pdf

Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective

Handling heterogeneity among units in quantile regression. Investigating the impact of students’ features on University outcome

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