Robust neural modeling for the cross-sectional analysis of accounting information
DOI10.1016/j.ejor.2005.10.064zbMath1109.62082OpenAlexW2017064439WikidataQ57896159 ScholiaQ57896159MaRDI QIDQ856316
Manuel Landajo, Pedro Lorca, Javier De Andrés
Publication date: 7 December 2006
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2005.10.064
simulationsartificial neural networksrobust regressionfirm sizeeconomic analysisaccounting ratiosfinancial ratiosNACE Code manufactoring
Applications of statistics in engineering and industry; control charts (62P30) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45)
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Cites Work
- Prediction of company acquisition in Greece by means of the rough set approach
- A hybrid intelligent system for predicting bank holding structures
- Approximation and estimation bounds for artificial neural networks
- Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics
- Multilayer feedforward networks are universal approximators
- Prediction of commercial bank failure via multivariate statistical analysis of financial structures: the Turkish case
- Forecasting business profitability by using classification techniques: a comparative analysis based on a Spanish case
- Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models
- Consequences and Detection of Misspecified Nonlinear Regression Models
- Sieve Extremum Estimates for Weakly Dependent Data
- Nonparametric estimation via empirical risk minimization
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