1,720,962 research outputs found
Time to default in credit scoring using survival analysis: a benchmark study
No full textWe investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context
Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models
No full textThe prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset
Contributions to the analysis of credit risk data using advanced survival analysis techniques.
No full textThe Basel Accords, a set of recommendations for regulating the banking industry, have changed the strategies of financial institutions significantly. These accords allowed banks to use risk assessment based on their own models to determine the size of the buffer capital they need to hold against unexpected losses. This gave rise to a more model-based focus in the banking industry. With typically a major focus on basic regression techniques such as logistic regression for modelling ``good'' versus ``bad'' customers, growing interest in methods from other areas of statistics and machine learning was a result. One of these areas is survival analysis.
Survival analysis deals with the analysis of the duration time until a certain event, such as the time of death in biological organisms. A typical property of survival analysis is the survival function S(t)=P(T>t), which is the probability that the time of the event of interest has not occurred by a stated time t.
Defining loan “default” (or, by extension, “early repayment”) as the event of interest, the appropriateness of using survival analysis in the credit risk content becomes apparent. The advantage of using this method here, as opposed to more frequently used classification techniques, is that
(1) It is possible to compute a ``probability of default'' or a PD-estimate at every point in time during the loan term.
(2) One can predict the expected time of default.
Despite the fact that there are certain analogies between medical survival and survival in a credit loan context, there are also differences that might make the standard survival approach inappropriate for the analysis of credit data. A basic assumption of survival analysis is that each subject will eventually experience the event of interest, but in reality, default will only take place for a small proportion of the population. To model a so-called ``insusceptible'' part of the loan population, mixture cure models can be used.
In this thesis, we explain the mixture cure model in more detail in Chapter 1. As there is there is a missing data-issue since there is incomplete information on which part of the population is ``susceptible'' to default (or early repayment) and which part is not, an appropriate version of Akaike's information criterion (AIC) for variable selection is derived and applied to these models.
Certain loan applicant characteristics that would affect the time of default or early repayment, might not be observed. In Chapter 2, this problem is addressed by incorporating ``unobserved heterogeneity'' in the mixture cure model. For model fitting purposes, a hierarchical expectation-maximization algorithm is derived.
In Chapter 3, we take a step outside the mixture cure framework, and perform a benchmark study comparing several survival techniques (both mixture and non-mixture survival models). These survival analysis techniques are applied to ten different data sets from five Belgian financial institutions, and evaluated using three different types of evaluation metrics: receiver operating characteristics curves, default time prediction and expected future values of the loan.
Chapter 4 extends the mixture cure model such that time-dependent covariates can be included. The method is applied to real life credit data including both personal (time-independent) information of the loan applicants and macro-economic factors that change over time, such as the unemployment and interest rates.status: Publishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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