How do heat and flood risk drive residential green infrastructure implementation in Phoenix,Arizona?

Urban Ecosystems - Green infrastructure is an increasingly popular strategy to simultaneously address challenges associated with urbanization and global environmental change, including increased...     

Urban or rural fragments: which soil use in forest surroundings induces greater litter deposition?

Urban Ecosystems - The soil use in surroundings of forest remnants, as well as, variations in seasonality and rainfall totals from each year might influence the composition, structure, and...     

Expert knowledge elicitation using item response theory

In an expert knowledge elicitation exercise, experts face a carefully constructed list of questions that they answer according to their knowledge. The elicitation process concludes when a probability distribution is found that adequately captures the experts' beliefs in the light of those answers. In many situations, it is very difficult to create a set of questions that will efficiently capture the experts' knowledge, since experts might not be able to make precise probabilistic statements about the parameter of interest. We present an approach for capturing expert knowledge based on item response theory, in which a set of binary response questions is proposed to the expert, trying to capture responses directly related to the quantity of interest. As a result, the posterior distribution of the parameter of interest will represent the elicited prior distribution that does not assume any particular parametric form. The method is illustrated by a simulated example and by an application involving the elicitation of rain prophets' predictions for the rainy season in the north-east of Brazil.     

Comparison of Ability Estimation Methods Using IRT for Tests with Different Degrees of Difficulty

The aim of the present study is to determine the dependence of the estimation of individual abilities obtained by item response theory (IRT) in relation to the degree of test difficulty and to evaluate how the estimation error may be affected by the estimation method employed. It is shown that abilities in the scale region with little test information are more efficiently estimated using the maximum weighted likelihood estimation (WLE) method, particularly abilities belonging to the upper part of the scale. The study also demonstrates the importance of largest tests for ability estimation.     

On the Robustness of Bayesian Modelling of Location and Scale Structures Using Heavy-Tailed Distributions

For solving conflicting information between data and prior distributions, Bayesian modelling with heavy-tailed distributions is applied. Exploiting properties of regularly varying functions and distribution functions as well as their relationship with the finiteness of the moments, we establish results for both location and shape parameter structures. And, as a side result, rates of convergence are derived.     

Transformed GARMA model: Properties and simulations

Real time series can present anomalies, like non-additivity, non-normality, and heteroscedasticity, which makes using GARMA models impossible. Our article introduces a new class of models called Transformed Generalized Autoregressive Moving Average (TGARMA) models that allow using transformations to guarantee the GARMA assumptions. We present an extensive simulation study of the influence of the transformation on GARMA estimation. We also propose using bootstrap methods to get more information about the distribution of the transformation parameter. We apply the methodology to data related to annual Swedish fertility rates.     

Attitudinal and structural drivers of preferred versus actual residential landscapes in a desert city

Urban Ecosystems - Residential landscaping decisions can have important implications for water use and conservation in urban areas. Yard preferences are generally closely related to actual yard...     

The Touchard distribution

We present a novel model, which is a two-parameter extension of the Poisson distribution. Its normalizing constant is related to the Touchard polynomials, hence the name of this model. It is a flexible distribution that can account for both under- or overdispersion and concentration of zeros that are frequently found in non-Poisson count data. In contrast to some other generalizations, the Hessian matrix for maximum likelihood estimation of the Touchard parameters has a simple form. We exemplify with three data sets, showing that our suggested model is a competitive candidate for fitting non-Poisson counts.     

“Shelf-Ready” Print Serials Acquisitions

The focus of this installment of “The Balance Point” is “shelf-ready” print serials acquisitions, including functions associated with traditional consolidation services (ordering, receiving, check-in, labeling, claiming and batch shipments), and the newer capabilities of uploading check-in data automatically into library systems. Featured authors discuss traditional vendor consolidation services, pilot projects and experiments in the pursuit of what they define as genuinely “shelf-ready” periodicals. The authors view obtaining “shelf-ready” print journal issues as an effective and efficient means of managing print serials operations while coping with the demands of managing digital resource acquisitions with limited financial resources.