How easy is a given density to estimate?
Computational Statistics & Data Analysis
Minumum Hellinger distance estimation for Poisson mixtures
Computational Statistics & Data Analysis
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Minimum distance estimation of GARCH(1,1) models
Computational Statistics & Data Analysis
Bootstrap prediction intervals for autoregressive time series
Computational Statistics & Data Analysis
Minimum disparity computation via the iteratively reweighted least integrated squares algorithms
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing
Mining local and tail dependence structures based on pointwise mutual information
Data Mining and Knowledge Discovery
Efficient Hellinger distance estimates for semiparametric models
Journal of Multivariate Analysis
Robust small sample accurate inference in moment condition models
Computational Statistics & Data Analysis
Minimum distance estimation in a finite mixture regression model
Journal of Multivariate Analysis
| Hi-index | 0.03 |
A simultaneously efficient and robust approach for distribution-free parametric inference, called the simulated minimum Hellinger distance (SMHD) estimator, is proposed. In the SMHD estimation, the Hellinger distance between the nonparametrically estimated density of the observed data and that of the simulated samples from the model is minimized. The method is applicable to the situation where the closed-form expression of the model density is intractable but simulating random variables from the model is possible. The robustness of the SMHD estimator is equivalent to the minimum Hellinger distance estimator. The finite sample efficiency of the proposed methodology is found to be comparable to the Bayesian Markov chain Monte Carlo and maximum likelihood Monte Carlo methods and outperform the efficient method of moments estimators. The robustness of the method to a stochastic volatility model is demonstrated by a simulation study. An empirical application to the weekly observations of foreign exchange rates is presented.