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MIT OPTIMIERUNGSANSÄTZEN BESSERE ENTSCHEIDUNGEN TREFFEN? – EINE EMPIRISCHE ANALYSE IN EINEM PLANSPIEL
Schoenau, Franziska; Musshoff, Oliver - 2011
In diesem Beitrag wird der Frage nachgegangen, inwieweit Optimierungsansätze wie lineareProgrammierungsmodelle geeignet sind, bessere Entscheidungen als reale Entscheider zu treffen.Auch beleuchtet werden Unterschiede im Entscheidungsverhalten und in denProduktionsstrategien. Dazu lassen wir...
Persistent link: https://www.econbiz.de/10009446182
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How capital structure adjusts dynamically during financial crises
Ariff, Mohamed; Taufiq, Hassan; Shamsher, Mohamad - 2008
The availability of a unique data set of financially distressed firms enabled this study to apply the dynamic capital structure adjustment model to a study of capital structure. In addition, the factors driving capital structure adjustment of financially distressed and of healthy firms were...
Persistent link: https://www.econbiz.de/10009441702
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Imputation and Dynamic Models in Semiparametric Survival Analysis.
Liu, Xiaohong - 2011
: (1) prediction of patients? lifetimes based on their risk profiles; (2) estimation of dynamic exposure effects on …
Persistent link: https://www.econbiz.de/10009482958
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Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
West, Mike - 2010
We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...
Persistent link: https://www.econbiz.de/10009475411
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ADOPT: a tool for predicting adoption of agricultural innovations
Kuehne, Geoff; Llewellyn, Rick S.; Pannell, David J.; … - 2011
reasoning underpinning the development of ADOPT (Adoption and Diffusion Outcome Prediction Tool). The tool has been designed to …
Persistent link: https://www.econbiz.de/10009446353
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Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation
Alonso, Andrés; Carbó Martinez, José Manuel - 2021
. ; In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by …
Persistent link: https://www.econbiz.de/10012525481
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Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation
Alonso, Andrés; Carbó Martinez, José Manuel - 2021
Summary of Banco de España Working Paper no. 2105
Persistent link: https://www.econbiz.de/10012526625