It is essential for most organizations and financial institutes to be able to evaluate their decision-making units (DMUs), when there is only a ratio of inputs to outputs (or vice versa) available. In this paper, we will propose our two-stage DEA-R models, which are a combination of data envelopment analysis and ratio data, based on value efficiency. Integrating value efficiency into data envel...

Both climate change and technical change are uncertain. In this paper we show the importance of including both uncertainties when modeling for policy analysis. We then develop an approach for incorporating uncertainty of technical change into climate change policy analysis. We define and demonstrate a protocol for bottom-up expert assessments about prospects for technologies. We then describe a...

INTRODUCTION Core outcome sets are standardised lists of outcomes, which should be measured and reported in all clinical studies of a specific condition. This study aims to develop core outcome sets for economic evaluations in asthma studies. Economic outcomes include items such as costs, resource use or quality-adjusted life years. The starting point in developing core outcome sets will be con...

We report two studies investigating how naïve reasoners evaluate the probability that a conditional assertion is true, and the conditional probability that the consequent of the conditional is true given that the antecedent is true. The mental model theory predicts that individuals should evaluate the probability of a conditional on the basis of the mental models representing the conditional, a...

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensio...

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these...

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of m...

BACKGROUND In non-randomised evaluations of public-health interventions, statistical methods to control confounding will usually be required. We review approaches to the control of confounding and discuss issues in drawing causal inference from these studies. METHODS Non-systematic review of literature and mathematical data-simulation. RESULTS Standard stratification and regression techniqu...

BACKGROUND Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. OBJECTIVES We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated th...