Search Results - subject:"Statistical Learning"

The impact of statistical learning on violations of the sure-thing principle

Nicholls, Nicky; Romm, Aylit Tina; Zimper, Alexander - In: Journal of risk and uncertainty : JRU 50 (2015) 2, pp. 97-115

Persistent link: https://www.econbiz.de/10011393046

Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

Acebes, Fernando; Pereda, María; Poza, David; Pajares … - In: International journal of project management : the … 33 (2015) 7, pp. 1597-1609

Persistent link: https://www.econbiz.de/10011381352

Partition clustering of High Dimensional Low Sample Size data based on P-Values

Von Borries, George Freitas - 2008

Doctor of Philosophy

Persistent link: https://www.econbiz.de/10009464047

The default risk of firms examined with smooth support vector machines

Härdle, Wolfgang Karl; Lee, Yuh-Jye; Schäfer, Dorothea; … - 2008

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth...

Persistent link: https://www.econbiz.de/10010274139

Interactive Learning and Behavioral Sunspots

Gaballo, Gaetano - Dipartimento di Politica Economica, Finanza e Sviluppo … - 2008

This paper shows how to extend the adaptive learning approach to a truly behavioral uncertainty problem. It investigates the simplest case of a first order self-referential model where two agents have a non-negligible impact on aggregate expectations. Unlike the standard setting, agents are not...

Persistent link: https://www.econbiz.de/10005766574

The Default Risk of Firms Examined with Smooth Support Vector Machines

Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2008

the decision task of loan o–cers. Keywords: Insolvency Prognosis, SVMs, Statistical Learning Theory, Non … of statistical learning (Burges, 1998; Cristianini and Shawe-Taylor, 2000; Vapnik, 1995) has become one of the most … data. This hyperplane is \optimal" in the sense of statistical learning: it strikes a balance between overfltting and …

Persistent link: https://www.econbiz.de/10005207945

Learning algorithms may perform worse with increasing training set size: Algorithm–data incompatibility

Yousef, Waleed A.; Kundu, Subrata - In: Computational Statistics & Data Analysis 74 (2014) C, pp. 181-197

In machine learning problems a learning algorithm tries to learn the input–output dependency (relationship) of a system from a training dataset. This input–output relationship is usually deformed by a random noise. From experience, simulations, and special case theories, most practitioners...

Persistent link: https://www.econbiz.de/10011056601

Prediction of time series by statistical learning: general losses and fast rates

Pierre, Alquier; Xiaoyin, Li; Olivier, Wintenberger - In: Dependence Modeling 1 (2014) January, pp. 65-93

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach …

Persistent link: https://www.econbiz.de/10011008551

Coarse decision making and overfitting

Al-Najjar, Nabil I.; Pai, Mallesh M. - In: Journal of Economic Theory 150 (2014) C, pp. 467-486

We study decision makers who willingly forgo decision rules that vary finely with available information, even though these decision rules are technologically feasible. We model this behavior as a consequence of using classical, frequentist methods to draw robust inferences from data. Coarse...

Persistent link: https://www.econbiz.de/10010743794

Simultaneously learning and optimizing using controlled variance pricing

Boer, Arnoud V. den; Zwart, Bert - In: Management science : journal of the Institute for … 60 (2014) 3, pp. 770-783

Persistent link: https://www.econbiz.de/10010347875