A self tuning model for risk estimation

Elliott, Robert J.; Filinkov, Alexei

doi:10.1016/j.eswa.2007.01.044

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…It should be stressed that other statistical techniques, such as support vector machines (e.g. Deschaine and Francone, 2008), smoothing nonparametric methods, timevarying models, mathematical programming, K-NN, fuzzy rules, kernel learning method, Markov Hoffmann et al, 2007;Hand and Henley, 1997;Baesens et al, 2003;Crook et al, 2007;Huang et al, 2007;Yang, 2007;Elliott and Filinkov, 2008;Bellotti and Crook, 2009).…”

Section: Advanced Statistical Methods Versus Traditional Statistical mentioning

confidence: 99%

“…Deschaine and Francone, ), smoothing nonparametric methods, time‐varying models, mathematical programming, K ‐NN, fuzzy rules, kernel learning method, Markov models and linear programming, have been discussed in the literature (e.g. Hoffmann et al , ; Hand and Henley, ; Baesens et al , ; Crook et al , ; Huang et al , ; Yang, ; Elliott and Filinkov, ; Bellotti and Crook, ).…”

Section: Review Of the Literaturementioning

confidence: 99%

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Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Abdou

Pointon

2011

Intell. Sys. Acc. Fin. Mgmt.

308

209

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SummaryCredit scoring has been regarded as a core appraisal tool of different institutions during the last few decades, and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria are investigated in the literature. The principal aim of this paper is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. This paper also aims to investigate how credit scoring has developed in importance, and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients" behaviour. Finally, this paper also suggests a number of directions for future research.

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Section: Advanced Statistical Methods Versus Traditional Statistical mentioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Abdou

Pointon

2011

Intell. Sys. Acc. Fin. Mgmt.

308

209

View full text Add to dashboard Cite

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“…It can obtain accurate results for different initial values, and the simulation time T need not be long. Therefore, our numerical algorithm improves existing methods for similar problems, such as those in Elliott et al (2007) and Elliott and Filinkov (2008). Moreover, Algorithm 1 is simple, stable and flexible when compared with current algorithms.…”

Section: Theorem 3 Suppose That T Is a Contraction And T Tends To +∞mentioning

confidence: 89%

“…To derive an explicit form of this function, we need to rewrite E[X (k) t |F N T ] in terms of the reference probability P Ď . By Bayes' rule (Elliott, 1982;Elliott et al, 2007), we have…”

Section: Remark 2 It Is Known From Vretbladmentioning

confidence: 99%

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Insurance claims modulated by a hidden Brownian marked point process

Elliott

Chen

Duan

2009

Insurance: Mathematics and Economics

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a b s t r a c tAimed at better modeling insurance claims in an economic environment driven by business cycles, a new Markov-modulated Poisson process model is proposed, and an algorithm is derived to estimate the hidden Markov process by using the observed information. Our method differs from existing ones in the following ways: the new hidden process can model more efficiently the cyclic state of the economic environment; our theory is based on a variation of the law of large numbers and is easy to understand; the Fourier expansion-based parameter estimation algorithm is flexible and can be more easily implemented than other algorithms. Simulation results not only demonstrate the practicality of our model and algorithm, but also show the efficiency and robustness of the estimation algorithm.

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