In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Effects of variations in water quantity and quality in the structure and functions of invertebrates’ community of a Mediterranean urban stream

Urban Ecosystems - Urban streams provide important ecosystem services to cities’ population, from the maintenance of urban biodiversity, temperature, humidity and air quality to improving...     

Maximum force levels in different positions of shoulder and elbow

The number of occupational diseases in Mexico is alarming and clearly it is important to do studies with the purpose of improving the design of workstations. The objective of this research is to determine the maximum force levels in different positions of shoulder and elbow. An experiment was conducted with 16 subjects between 18 and 28 years old: 8 male and 8 female. We considered 16 different positions, working with the right and left arm to perform the tasks of pull and push. The tasks consisted of pushing or pulling a dynamometer for a period of 3 seconds as hard as possible. The results were presented in tables. The tables show the mean, standard deviation and range of force levels in different positions.     

Influence of door handles design in effort perception: accessibility and usability

The application of ergonomics in product design is essential to its accessibility and usability. The development of manual devices should be based on ergonomic principles. Effort perception analysis is an essential approach to understand the physical and subjective aspects of the interface. The objective of the present study was to analyze the effort perception during a simulated task with different door handles by Portuguese subjects of both genders and different ages. This transversal study agreed with ethical aspects. 180 subjects of both genders pertaining to three age groups have participated. Five door handles with different shapes were evaluated. A subjective numeric rating scale of 5 levels was used to evaluate the effort. For statistical analysis it was applied the Friedman non-parametric test. The results have showed no significant differences of effort perception in door handles "A" and "B"; "A" and "D"; and "D" and "C". Door handle "E" presented the lowest values of all. In general, there's an inverse relationship between the results of biomechanical studies and the effort perception of the same task activity. This shows that door handles design influence directly these two variables and can interfere in the accessibility and usability of these kinds of products.     

Characterizing Environmental Harm: Developments in an Approach to Strategic Risk Assessment and Risk Management

Environmental policymakers and regulators are often in the position of having to prioritize their actions across a diverse range of environmental pressures to secure environmental protection and improvements. Information on environmental issues to inform this type of strategic analysis can be disparate; it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy analysis and comparison. Nevertheless, judgments are required on the significance of various environmental pressures and on the inherent uncertainties to inform strategic assessments such as “state of the environment” reports. How can decisionmakers go about this type of strategic and comparative risk analysis? In an attempt to provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has conducted a program of developmental research on strategic risk assessment since 1996. The tools developed under this program use the concept of “environmental harm” as a common metric, viewed from technical, social, and economic perspectives, to analyze impacts from a range of environmental pressures. Critical to an informed debate on the relative importance of these perspectives is an understanding and analysis of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to actual or potential environmental harm from a range of hazards. Recent developments in our approach, described herein, allow a presentation of the analysis in a structured fashion so as to better inform risk‐management decisions.     

Sequential parameter learning and filtering in structured autoregressive state-space models

We present particle-based algorithms for sequential filtering and parameter learning in state-space autoregressive (AR) models with structured priors. Non-conjugate priors are specified on the AR coefficients at the system level by imposing uniform or truncated normal priors on the moduli and wavelengths of the reciprocal roots of the AR characteristic polynomial. Sequential Monte Carlo algorithms are considered and implemented for on-line filtering and parameter learning within this modeling framework. More specifically, three SMC approaches are considered and compared by applying them to data simulated from different state-space AR models. An analysis of a human electroencephalogram signal is also presented to illustrate the use of the structured state-space AR models in describing biomedical signals.     

Bayesian factor models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.     

Transient Analysis of Large Markov Models with Absorbing States Using Regenerative Randomization

ABSTRACT

In this article, we develop a new method, called regenerative randomization, for the transient analysis of continuous time Markov models with absorbing states. The method has the same good properties as standard randomization: numerical stability, well-controlled computation error, and ability to specify the computation error in advance. The method has a benign behavior for large t and is significantly less costly than standard randomization for large enough models and large enough t. For a class of models, class C, including typical failure/repair reliability models with exponential failure and repair time distributions and repair in every state with failed components, stronger theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. A large example belonging to that class is used to illustrate the performance of the method and to show that it can indeed be much faster than standard randomization.     

Work Trajectories of Female and Male Immigrants in Spain

This article aims to examine in depth the work trajectories of individuals over time, in order to provide a wider perspective of the employment history of immigrants compared to native people, by gender and for more than one decade (2005–2017) in Spain. We use microdata (cohort and multivariate analysis) from the Labour Force Survey and carry out a comparison for three groups: the Spanish born in Spain, the Spanish born abroad, and the non-Spanish born abroad. The results confirm that the non-Spanish born-abroad group is characterized by the existence of segmented assimilation. All foreigners suffered a loss in their work trajectories, since their employment rate in 2017 has as yet neither reached the level of the last years of the previous economic expansion nor the level of the previous cohorts at the same age.     

Measuring and Analysing Community Action for Health: An Indicator-Based Typology and Its Application to the Case of Barcelona

Social welfare systems face major challenges, particularly in a context of social transformation, austerity and growing inequalities. This process is highly visible in the health sector. In this context, many voices ask for public sector reforms and community action for health as a relevant practice. However, analyses and evaluations of this kind of practices are still limited, particularly beyond the cases of single community health actions or interventions. We still need to identify key indicators for measuring and characterising what community action for health consists of, as well as to what degree this kind of intervention has been developed across a city. Based on a research about 49 neighborhoods in Barcelona, this paper creates an index to measure and characterize community action for health, using different indicators: citizen engagement programs in community health, organizational transformation of the health and social protection systems, stable participatory structures with specific teams, and urban health policies. We apply the index to the case of Barcelona and build a map of community action for health in the city using 4 categories: strong community health development (one neighbourhood), middle (9 neighborhoods), emergent (25 neighborhoods) and without specific community health promotion (14 neighbourhoods). We find that community action for health is extensive within the city of Barcelona, have great potential as a response to the need for change in the relationship between the public (health) sector and the citizenry, but is still implemented unequally across the urban territory in terms of types and methods.