Formal and Informal Model Selection with Incomplete Data

Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the complete-data situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature. We argue that model assessment ought to consist of two parts: (i) assessment of a model's fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones.Comment: Published in at http://dx.doi.org/10.1214/07-STS253 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Discussion of Likelihood Inference for Models with Unobservables: Another View

Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303]Comment: Published in at http://dx.doi.org/10.1214/09-STS277A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Focusing on out-of-focus : assessing defocus estimation algorithms for the benefit of automated image masking

Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper

A conceptual model of service exchange in service-dominant logic

The service system is the basic abstraction of Service Science. This paper proposes the Resource-Service-System model as a conceptual model of service systems interacting in service exchanges, assuming a service-dominant logic economic worldview. The paper explains how the model was developed starting from the Resource-Event-Agent business model ontology, taking into account insights gained from studying Service Science literature and existing service ontologies. The paper also explains how different model views can contribute to study various aspects of service systems and exchanges

Hypoxic-ischemic encephalopathy: facts and insights

Asphyxia may cause a hypoxic-ischemic encephalopathy due to an insufficient supply of oxygenated blood to the brain, which leads to a cerebral palsy. The transition state between both is called neonatal encephalopathy. However, there are many other causes for neonatal encephalopathy, such as developmental abnormalities, metabolic abnormalities, autoimmune disorders, coagulation disorders, infections, trauma, IUGR, and chromosomal abnormalities. Therefore, the American College of Obstetricians and Gynecologists defined criteria, sufficient for an acute intrapartum event to cause a cerebral palsy. Asphyxia itself causes a redistribution of the blood flow towards the central organs, the brain and heart, but in a further stage the cerebral energy metabolism may breakdown and a cascade of events will occur: energy, i.e. ATP, fails for Na+/K+ pumps, the gradients of Na+/K+ across the cell membranes cannot any longer be maintained and cell oedema develops, furthermore, glutamate is released and exerts a neurotoxic influence, Ca2+ overflows the cells and activates lipases, proteases and nucleases, the cellular protein synthesis decreases, after the insult reperfusion of the tissue occurs, but oxygen radicals with a nefast influence are formed, interleukins are released and an inflammatory reaction takes place and proto-oncogenes are expressed. Cell death is an inevitable consequence, leading to the hypoxic-ischemic encephalopathy

Naturalism

1. Introduction 2. Naturalism in the First Half of the Century 3. Three Eminent Figures 3.1 Husserl 3.2 Wittgenstein 3.3 Quine 4. The Nature of Naturalism 5. A Classification of Naturalisms 5.1 Metaphysical Naturalism 5.2 Methodological, or Scientific, Naturalism 5.2.1 Naturalism with a Leading Science: Physicalism and Biologism 5.2.2 Naturalism without a Leading Science 5.3. Analytic, or Semantic, Naturalism 6. Three Fields of Naturalisation 6.1 Naturalising Epistemology 6.2 Naturalising Intentionality 6.3 Naturalising Normativity 7. Naturalism and Human Nature 8. Scientific naturalism quo vadis? 8.1 Scientia mensura and the Disunity of the Special Sciences 8.2 The Business of Philosoph