2,489 research outputs found
Do Soup Kitchen Meals Contribute to Suboptimal Nutrient Intake & Obesity in the Homeless Population?
The double burden of suboptimal nutrient intake and obesity exists when available foods lack essential nutrients to promote health and provide high amounts of energy. This study evaluated the nutrition content of 41 meals served to the homeless at 3 urban soup kitchens. The mean nutrient content of all meals and of meals from each of the kitchens was compared to two-thirds of the estimated average requirement (EAR). The mean nutrient content of the meals did not provide two-thirds of the EAR for energy, vitamin C, magnesium, zinc, dietary fiber, or calcium but provided 11.8% of calories from saturated fat. On average one meal did not meet homeless individuals’ estimated requirements; however, 2 meals did meet estimated requirements but provided inadequate fiber and high amounts of energy, saturated fat, and sodium. Soup kitchen meals may contribute to the high prevalence of obesity and chronic disease reported in the homeless, food insecure population
Likelihood-based inference for max-stable processes
The last decade has seen max-stable processes emerge as a common tool for the
statistical modeling of spatial extremes. However, their application is
complicated due to the unavailability of the multivariate density function, and
so likelihood-based methods remain far from providing a complete and flexible
framework for inference. In this article we develop inferentially practical,
likelihood-based methods for fitting max-stable processes derived from a
composite-likelihood approach. The procedure is sufficiently reliable and
versatile to permit the simultaneous modeling of marginal and dependence
parameters in the spatial context at a moderate computational cost. The utility
of this methodology is examined via simulation, and illustrated by the analysis
of U.S. precipitation extremes
Bayesian threshold selection for extremal models using measures of surprise
Statistical extreme value theory is concerned with the use of asymptotically
motivated models to describe the extreme values of a process. A number of
commonly used models are valid for observed data that exceed some high
threshold. However, in practice a suitable threshold is unknown and must be
determined for each analysis. While there are many threshold selection methods
for univariate extremes, there are relatively few that can be applied in the
multivariate setting. In addition, there are only a few Bayesian-based methods,
which are naturally attractive in the modelling of extremes due to data
scarcity. The use of Bayesian measures of surprise to determine suitable
thresholds for extreme value models is proposed. Such measures quantify the
level of support for the proposed extremal model and threshold, without the
need to specify any model alternatives. This approach is easily implemented for
both univariate and multivariate extremes.Comment: To appear in Computational Statistics and Data Analysi
Models for extremal dependence derived from skew-symmetric families
Skew-symmetric families of distributions such as the skew-normal and skew-
represent supersets of the normal and distributions, and they exhibit
richer classes of extremal behaviour. By defining a non-stationary skew-normal
process, which allows the easy handling of positive definite, non-stationary
covariance functions, we derive a new family of max-stable processes - the
extremal-skew- process. This process is a superset of non-stationary
processes that include the stationary extremal- processes. We provide the
spectral representation and the resulting angular densities of the
extremal-skew- process, and illustrate its practical implementation
(Includes Supporting Information).Comment: To appear in Scandinavian Journal of Statistic
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