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We propose a non-parametric methodology to study the presence of economies of scope between teaching and research (i.e., the teaching–research nexus). In particular, the paper advocates a conditional version of the ‘benefit-of-the-doubt’ approach to estimate the relationship between professors’ overall academic output, measured by a composite measure of multi-dimensional and importance-adjusted scores of teaching effectiveness and research productivity, and the time devoted to teaching and to research. The methodology is illustrated with a dataset of professors working at a Business & Administration department of a university college where the time allocation of teaching and research was assigned exogenously. The outcome of the analysis indicates the presence of limited scope economies for professors with an extensive research time. 相似文献
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Laurens Beran 《Journal of applied statistics》2014,41(6):1332-1341
For classification problems where the test data are labeled sequentially, the point at which all true positives are first identified is often of critical importance. This article develops hypothesis tests to assess whether all true positives have been labeled in the test data. The tests use a partial receiver operating characteristic (ROC) that is generated from a labeled subset of the test data. These methods are developed in the context of unexploded ordnance (UXO) classification, but are applicable to any binary classification problem. First, the likelihood of the observed ROC given binormal model parameters is derived using order statistics, leading to a nonlinear parameter estimation problem. I then derive the approximate distribution of the point on the ROC at which all true instances are found. Using estimated binormal parameters, this distribution can be integrated up to a desired confidence level to define a critical false alarm rate (FAR). If the selected operating point is before this critical point, then additional labels out to the critical point are required. A second test uses the uncertainty in binormal parameters to determine the critical FAR. These tests are demonstrated with UXO classification examples and both approaches are recommended for testing operating points. 相似文献
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Laurens M. Bouwer 《Risk analysis》2013,33(5):915-930
Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state‐of‐the‐art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra‐tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects. 相似文献
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Long‐range dependence (LRD) refers to dependence structures that decay slowly with increasing distance. Mathematically this leads to limit theorems that differ from the short‐memory case, and to major corrections of standard statistical methods. Here, a brief overview of the probabilistic foundations and statistical methods is given. We focus on how LRD is defined, which typical models may generate LRD, how to do statistical inference for stationary and nonstationary long‐memory models, and how to distinguish between LRD and alternative models that may mimic long‐memory behavior. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under:
- Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data