893 resultados para Credit generalization


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Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

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Information skills instruction for research candidates bas recently been formalised as coursework at the Queensland University of Technology. Feedback solicited from participants suggests that students benefit from such coursework in a number of ways. Their perception of the value of specific content areas to their literature review and thesis presentation is favourable. A small group of students who participated in Interviews identified five ways in which the coursework assisted the research process. As Instructors continue to work with the post·graduate community it would be useful to deepen our understanding of how such instruction is perceived and the benefits which can be derived from it.

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A business process is often modeled using some kind of a directed flow graph, which we call a workflow graph. The Refined Process Structure Tree (RPST) is a technique for workflow graph parsing, i.e., for discovering the structure of a workflow graph, which has various applications. In this paper, we provide two improvements to the RPST. First, we propose an alternative way to compute the RPST that is simpler than the one developed originally. In particular, the computation reduces to constructing the tree of the triconnected components of a workflow graph in the special case when every node has at most one incoming or at most one outgoing edge. Such graphs occur frequently in applications. Secondly, we extend the applicability of the RPST. Originally, the RPST was applicable only to graphs with a single source and single sink such that the completed version of the graph is biconnected. We lift both restrictions. Therefore, the RPST is then applicable to arbitrary directed graphs such that every node is on a path from some source to some sink. This includes graphs with multiple sources and/or sinks and disconnected graphs.

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CAAS is a rule-based expert system, which provides advice on the Victorial Credit Act 1984. It is currently in commercial use, and has been developed in conjunction with a law firm. It uses an object-oriented hybrid reasoning approach. The system was initially prototyped using the expert system shell NExpert Object, and was then converted into the C++ language. In this paper we describe the advantages that this methodology has, for both commercial and research development.

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This paper investigates the productivity change of Japanese credit banks with a Malmquist index and the input technological bias during 2000-2006. Our results indicate that the traditional growth accounting method, which assumes Hicks neutral technological change, is not appropriate for analyzing changes in productivity. Our analysis unambiguously shows that management of Shinkin banks has to be improved. These must be based on the improvement of technical efficiency and/or technological change, emulating the procedures of the best-practice banks, i.e., those banks with Malmquist productivity scores higher than one and simultaneously with technical efficiency and technological change higher than one.

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This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

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Biological factors underlying individual variability in fearfulness and anxiety have important implications for stress-related psychiatric illness including PTSD and major depression. Using an advanced intercross line (AIL) derived from C57BL/6 and DBA/2J mouse strains and behavioral selection over 3 generations, we established two lines exhibiting High or Low fear behavior after fear conditioning. Across the selection generations, the two lines showed clear differences in training and tests for contextual and conditioned fear. Before fear conditioning training, there were no differences between lines in baseline freezing to a novel context. However, after fear conditioning High line mice demonstrated pronounced freezing in a new context suggestive of poor context discrimination. Fear generalization was not restricted to contextual fear. High fear mice froze to a novel acoustic stimulus while freezing in the Low line did not increase over baseline. Enhanced fear learning and generalization are consistent with transgenic and pharmacological disruption of the hypothalamic-pituitary-adrenal axis (HPA-axis) (Brinks, 2009, Thompson, 2004, Kaouane, 2012). To determine whether there were differences in HPA-axis regulation between the lines, morning urine samples were collected to measure basal corticosterone. Levels of secreted corticosterone in the circadian trough were analyzed by corticosterone ELISA. High fear mice were found to have higher basal corticosterone levels than low line animals. Examination of hormonal stress response components by qPCR revealed increased expression of CRH mRNA and decreased mRNA for MR and CRHR1 in hypothalamus of high fear mice. These alterations may contribute to both the behavioral phenotype and higher basal corticosterone in High fear mice. To determine basal brain activity in vivo in High and Low fear mice we used manganese-enhanced magnetic resonance imaging (MEMRI). Analysis revealed a pattern of basal brain activity made up of amygdala, cortical and hippocampal circuits that was elevated in the High line. Ongoing studies also seek to determine the relative balance of excitatory and inhibitory tone in the amygdala and hippocampus and the neuronal structure of its neurons. While these heterogeneous lines are selected on fear memory expression, HPA-axis alterations and differences in hippocampal activity segregate with the behavioral phenotypes. These differences are detectable in a basal state strongly suggesting these are biological traits underlying the behavioral phenotype (Johnson et al, 2011).

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Giving “extra credit” work to students has been a controversial and hotly debated pedagogical issue for the last 20 years (Blood et al. 1993; Groves 2000; Muztaba Fuad and Jones 2012; Norcross et al. 1989; Weimer 2011). Previous work has focused on the faculty perspective discussing benefits and drawbacks associated with extra credit work (e.g. Hill et al. 1993; Norcross et al. 1989). Other scholars have investigated the use and effects of pop quizzes and other extra credit assignments on students’ final grades (Thorne 2000; Oley 1993). Some authors have criticized that the empirical exploration of understanding students’ motivational and performance efforts remains scarce and “rarely appears in the literature” (Mays and Bower 2005, p. 1). Besides a gap of empirical work it further appears that most existing studies stem from Psychology or Information Science. Yet it is surprising that, even though the topic of extra credit is considered a common practice in marketing education (Ackerman and Kiesler 2007), there is a wide gap within the marketing education literature. For example, a quick search in the Journal of Marketing Education for the keyword “extra credit” shows only 25 search results; yet none of those papers address motivational or performance effects of extra credit. A further search in Marketing Education Review yielded no results at all. To the authors’ knowledge, the topic has only been addressed once by Ackerman and Kiesler in the 2007 MEA Proceedings who conclude that for “such a common part of the marketing education curriculum, we know surprisingly little about its impact on students” (p. 123).

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This work investigates the role of narrative literature in late-20th century and contemporary Anglo-American moral philosophy. It aims to show the trend of reading narrative literature for purposes of moral philosophy from the 1970 s and early 80 s to the present day as a part of a larger movement in Anglo-American moral philosophy, and to present a view of its significance for moral philosophy overall. Chapter 1 provides some preliminaries concerning the view of narrative literature which my discussion builds on. In chapter 2 I give an outline of how narrative literature is considered in contemporary Anglo-American moral philosophy, and connect this use to the broad trend of neo-Aristotelian ethics in this context. In chapter 3 I connect the use of literature to the idea of the non-generalizability of moral perception and judgment, which is central to the neo-Aristotelian trend, as well as to a range of moral particularisms and anti-theoretical positions of late 20th century and contemporary ethics. The joint task of chapters 2 and 3 is to situate the trend of reading narrative literature for the purposes of moral philosophy in the present context of moral philosophy. In the following two chapters, 4 and 5, I move on from the particularizing power of narrative literature, which is emphasized by neo-Aristotelians and particularists alike, to a broader under-standing of the intellectual potential of narrative literature. In chapter 4 I argue that narrative literature has its own forms of generalization which are enriching for our understanding of the workings of ethical generalizations in philosophy. In chapter 5 I discuss Iris Murdoch s and Martha Nussbaum s respective ways of combining ethical generality and particularity in a philosophical framework where both systematic moral theory and narrative literature are taken seriously. In chapter 6 I analyse the controversy between contemporary anti-theoretical conceptions of ethics and Nussbaum s refutation of these. I present my suggestion for how the significance of the ethics/literature discussion for moral philosophy can be understood if one wants to overcome the limitations of both Nussbaum s theory-centred, equilibrium-seeking perspective, and the anti-theorists repudiation of theory. I call my position the inclusive approach .

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We consider an enhancement of the credit risk+ model to incorporate correlations between sectors. We model the sector default rates as linear combinations of a common set of independent variables that represent macro-economic variables or risk factors. We also derive the formula for exact VaR contributions at the obligor level.

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The Ball-Larus path-profiling algorithm is an efficient technique to collect acyclic path frequencies of a program. However, longer paths -those extending across loop iterations - describe the runtime behaviour of programs better. We generalize the Ball-Larus profiling algorithm for profiling k-iteration paths - paths that can span up to to k iterations of a loop. We show that it is possible to number suchk-iteration paths perfectly, thus allowing for an efficient profiling algorithm for such longer paths. We also describe a scheme for mixed-mode profiling: profiling different parts of a procedure with different path lengths. Experimental results show that k-iteration profiling is realistic.