A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log–log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises. 相似文献
Background: Sub-Saharan Africa (SSA) countries are facing an epidemiological shift from infectious disease to chronic diseases, such as cardiovascular diseases (CVDs). CVDs incidence in SSA are frequently attributed to the prevalence of hypertension, diabetes, and overweight/obesity. Nevertheless, some researchers contend that CVDs are not a priority public health problem in SSA.
Method: This paper systematically reviews the evidence on CVDs and their relation with hypertension, diabetes mellitus and obesity/overweight in Ghana, Nigeria, South Africa, Sudan and Tanzania. The publication’s content was analyzed qualitatively using the directed content analysis method and the results were presented in a tabular format.
Result: The paper illustrates the rising prevalence of CVDs as well as the three related risk conditions in the selected SSA countries.
Conclusion: The review indicates a poor health system response to the increasing risk of CVDs in SSA. The conditions and major drivers that contribute to this underlying increasing trend need to be further studied. 相似文献
In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented. 相似文献
Given the recent increase in dust‐induced lung disease among U.S. coal miners and the respiratory hazards encountered across the U.S. mining industry, it is important to enhance an understanding of lung disease trends and the organizational contexts that precede these events. In addition to exploring overall trends reported to the Mine Safety and Health Administration (MSHA), the current study uses MSHA's enforcement database to examine whether or not compliance with health regulations resulted in fewer mine‐level counts of these diseases over time. The findings suggest that interstitial lung diseases were more prevalent in coal mines compared to other mining commodities, in Appalachian coal mines compared to the rest of the United States, and in underground compared to surface coal mines. Mines that followed a relevant subset of MSHA's health regulations were less likely to report a lung disease over time. The findings are discussed from a lung disease prevention strategy perspective. 相似文献