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Cost-efficient Variable Selection Using Branching LARS
Yue, Li Hua - 2010
Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between...
Persistent link: https://www.econbiz.de/10009447237
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Model selection and error estimation
Bartlett, P. L.; Boucheron, S.; Lugosi, G. - 2002
between error estimation and data-based complexity penalization: any good error estimate may be converted into a data …
Persistent link: https://www.econbiz.de/10009438376
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Contributions to the Analysis of Experiments Using Empirical Bayes Techniques
Delaney, James Dillon - 2006
Specifying a prior distribution for the large number of parameters in the linear statistical model is a difficult step in the Bayesian approach to the design and analysis of experiments. Here we address this difficulty by proposing the use of functional priors and then by working out important...
Persistent link: https://www.econbiz.de/10009475773
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Robust methods for analyzing multivariate responses with application to time-course data
Kim, Ji Young - 2010
of methods have been developed including Lasso. The group Lasso is an extension of the Lasso with the goal of selecting … group Lasso algorithms for the multivariate time-course data, and illustrate the robustness properties of the proposed …
Persistent link: https://www.econbiz.de/10009477900
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Sparse Estimation and Inference for Censored Median Regression
Shows, Justin Hall - 2009
an inverse censoring probability weighted least absolute deviation subject to the adaptive LASSO penalty. We show that …
Persistent link: https://www.econbiz.de/10009431200
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Business Sector Classification And Beyond Using Machine Learning
El propósito de este documento es presentar el trabajo sobre la sectorización y clasificación de Holdings usando Machine Learning (en español, Aprendizaje Automático) que se ha desarrollado en la Central de Balances en el Banco de España durante el último año. Este trabajo también ha...
Persistent link: https://www.econbiz.de/10014513240
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Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
Dunson, David - 2009
This article considers a methodology for flexibly characterizing the relationship between a response and multiple predictors. Goals are (1) to estimate the conditional response distribution addressing the distributional changes across the predictor space, and (2) to identify important predictors...
Persistent link: https://www.econbiz.de/10009475527
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Insolvenzprognose für ukrainische, deutsche sowie nordamerikanische Unternehmen
Perederiy, Volodymyr - 2010
Kennzahlen.Die zweite Methode verwendet das innovative statistische Lasso-Verfahren zur Kennzahlenauswahl im Rahmen eines … Insolvenzprognosemodells für US-amerikanische Grossunternehmen. Lasso ist ein neues vielversprechendes Verfahren zur Auswahl erklärender … weitere erklärende Variablen für Insolvenzprognose zu verwenden.Das Lasso-Verfahren wurde auch bei diesen Untersuchungen mit …
Persistent link: https://www.econbiz.de/10009460748