Actions
Incoming Resources
- Nonparamatric adaptive learning with feedback
- Forecast evaluation with shared data sets
- Artificial neural networks, an econometric perspective
- Nonparametric adaptive learning with feedback
- Maximum likelihood and the bootstrap for nonlinear dynamic models
- Regularized neural networks, some convergence rate results
- James-Stein type estimators in large samples with application to the least absolute deviation estimator
- Consistent nonparametric estimation and testing for the variance of a diffusion from discretely sampled observations
- Recursive M-estimation, nonlinear regression and neural network learning with dependent observations
- A subsampling approach to estimating the distribution of diverging statistics with applications to assessing financial market risks
- James-Stein type estimators in large samples with application to the least absolute deviation estimator
- Testing for structural change in some simple time series models
- Improved rates and asymptotic normality for nonparametric neural network estimators
- Recent advances and future directions in causality, prediction, and specification analysis, essays in honor of Halbert L. White Jr., Xiaohong Chen, Norman R. Swanson, editors
- A model selection approach to assessing the information in the term structure using linear models and artificial neural networks
- Data-snooping, technical trading rule performance and the bootstrap
- Estimation, inference and specification testing for possibly misspecified quantile regression
- Asymptotic properties of S-estimators for nonlinear regression models with dependent, heterogenous processes
- Dynamic econometric modeling, proceedings of the Third International Symposium in Economic Theory and Econometrics ; edited by William A. Barnett, Ernst R. Berndt, Halbert White
- Strong convergence of recursive M-estimators for models with dynamic latent variables
- Closed form integration of artificial neural networks with some applications to finance
- On learning the derivatives of an unknown mapping with multilayer feedforward networks
- Essays in honor of Jerry Hausman, edited by Badi H. Baltagi ... [and others]
- Hypernormal densities
- Testing conditional independence via empirical
- A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks
- Asymptotic properties of some projection-based Robbins-Monro procedures in a Hilber space
- Data-snooping, technical trading rule performance, and the bootstrap
- Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives
- Dynamic econometric modeling, proceedings of the Third International Symposium in Economic Theory and Econometrics ; edited by William A. Barnett, Ernst R. Berndt, Halbert White
- A consistent characteristic-function-based test for conditional independence
- Some convergence results for learning in recurrent neural networks
- Asymptotic and Bayesian confidence intervals for Sharple style weights
- Central limit and functional central limit theorems for Hilbert space-valued dependent processes
- Strong convergence of recursive m-estimators for models with dynamic latent variables
- An alternative definition of finite sample breakdown point with applications to regression model estimators
- Information criteria for selecting possibly misspecified parametric models
- Comments on testing economic theories and the use of model selection criteria
- The bootstrap of the mean for dependent heterogeneous arrays
- Recursive m-estimation, nonlinear regression and neural network learning with dependent observations
- A convergence result for learning in recurrent neural networks
- Bootstrapping the information matrix test
- Consistent specification testing with unidentified nuisance parameters using duality and Banach space limit theory
- The dangers of data-driven inference, the case of calendar effects in stock returns
- A unified theory of estimation and inference for nonlinear dynamic models, A. Ronald Gallant and Halbert White
- Hypernormal densities
- Asymptotic and bayesian confidence intervals for sharpe style weigths
- Approximating and learning unknown mappings using multilayer feedforward networks with bounded weights
- On more robust estimation of skewness and kurtosis, simulation and application to the S&P500 index
- Asymptotic properties of some projection-based Robbins-Monro procedures in a Hilbert space