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Automatic model selection: a new instrument for social science
Hendry, David F.; Krolzig, Hans-Martin - 2004
The properties of automatic model selection are discussed, focusing on PcGets. We explain the background concepts and why automatic methods can perform well. Criticisms of model selection procedures are noted and rebutted. The algorithm is sketched, distinguishing between costs of search and...
Persistent link: https://www.econbiz.de/10009441474
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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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The economic impact of conflict-related and policy uncertainty shocks: the case of Russia
Diakonova, Marina; Ghirelli, Corinna; Molina Sánchez, Luis
En el presente artículo se muestra como la incertidumbre política y las variables que miden el conflicto impactan sobre la actividad económica en Rusia (y en concreto sobre el PIB). Para ello se utilizan diversos indicadores que miden el conflicto, referidos a aspectos específicos de este...
Persistent link: https://www.econbiz.de/10013458112
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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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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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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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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
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Improving the performance of model-order selection criteria by partial-model selection search
Alkhaldi, Weaam; Iskander, D. Robert; Zoubir, Abdelhak M. - 2010
The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each...
Persistent link: https://www.econbiz.de/10009438286