Tail density estimation for exploratory data analysis using kernel methods

It is often critical to accurately model the upper tail behaviour of a random process. Nonparametric density estimation methods are commonly implemented as exploratory data analysis techniques for this purpose and can avoid model specification biases implied by using parametric estimators. In particular, kernel-based estimators place minimal assumptions on the data, and provide improved visualisation over scatterplots and histograms. However kernel density estimators can perform poorly when estimating tail behaviour above a threshold, and can over-emphasise bumps in the density for heavy tailed data. We develop a transformation kernel density estimator which is able to handle heavy tailed and bounded data, and is robust to threshold choice. We derive closed form expressions for its asymptotic bias and variance, which demonstrate its good performance in the tail region. Finite sample performance is illustrated in numerical studies, and in an expanded analysis of the performance of global climate models.     

Disentangling an avian assemblages’ evolutionary and functional history in a Chihuahuan desert city

Urban Ecosystems - Urban green spaces have been shown to be important hotspots of biodiversity in cities of temperate and humid/semihumid tropical ecoregions. Nonetheless, whether this pattern...     

Re-distributed manufacturing and the food-water-energy nexus: opportunities and challenges

Abstract

Addressing the intersection of two important emerging research areas, re-distributed manufacturing (RDM) and the food-energy-water (FEW) nexus, this work combines insights from engineering, business and policy perspectives and explores opportunities and challenges towards a more localized and sustainable food system. Analysis centred on two specific food products, namely bread and tomato paste reveals that the feasibility and potential of RDM vary with the type of food product and the supply chain (SC) components. Physically, energy efficiency, water consumption and reduction of waste and carbon footprint may be affected by scale and location of production activities and potentials of industrial symbiosis. From the business perspective, novel products, new markets and new business models are expected in order for food RDM to penetrate within the established food industry. Studies on policies, through the lens of public procurement, call for solid evidence of envisioned environmental, social and economic benefits of a more localized food system. An initial integrated framework is proposed for understanding and assessing food RDM and the FEW nexus.     

ALL IN THE FAMILY: Red States,Blue States,and Postmodern Family Patterns, 2000 and 2004

In this article, we propose a new way of understanding presidential election outcomes in red and blue states in 2000 and 2004, one that takes into account state‐level variation in postmodern family patterns. Using data from the Statistical Abstract, Census, the American Community Survey, and National Vital Statistics Reports, we construct two measures of state patterns of postmodern family formation (a father‐absent family scale, and a small/delayed family scale). We find that these patterns of postmodern family formation are powerful predictors of states' percentage of votes cast for the Democratic candidate in 2000 and 2004, even after controlling for differences in the composition of states' populations and for differences in states' economic characteristics (rates of economic growth, unemployment, and poverty). We suggest ways that this approach could contribute to the literature on how individual‐level factors, such as demographic characteristics and moral values, shape voting behavior and electoral outcomes.     

Risk vulnerability: a graphical interpretation

The article gives a graphical interpretation of the concept of risk vulnerability. It shows that in a specific context of binary lotteries the assumption of risk vulnerability adds to prudence what the assumption of decreasing absolute risk aversion adds to risk aversion. We end the presentation showing that results can be extended to the concept of multiplicative risk vulnerability.     

From Rags to Riches? Immigration and Poverty in Spain

This article for first time explores the relationship between immigration and poverty in Spain. Using recent Spanish household surveys, it is found, first, that both moderate and severe poverty are more acute among immigrants than among nationals and social transfers play no substantial role in reducing monetary deprivation in the case of foreign-born population; in the second place, we perform an econometric analysis that shows that the different poverty risk faced by local and immigrant households is not driven by differences in basic household and demographic characteristics.     

Markov-switching generalized additive models

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation. We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate.     

Fast and robust estimators of variance components in the nested error model

Usual fitting methods for the nested error linear regression model are known to be very sensitive to the effect of even a single outlier. Robust approaches for the unbalanced nested error model with proved robustness and efficiency properties, such as M-estimators, are typically obtained through iterative algorithms. These algorithms are often computationally intensive and require robust estimates of the same parameters to start the algorithms, but so far no robust starting values have been proposed for this model. This paper proposes computationally fast robust estimators for the variance components under an unbalanced nested error model, based on a simple robustification of the fitting-of-constants method or Henderson method III. These estimators can be used as starting values for other iterative methods. Our simulations show that they are highly robust to various types of contamination of different magnitude.