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the paper develops a nonparametric estimation method for the case where the samples are obtained from using a resampling algorithm. The asymptotic theory shows that in each case the rate of convergence of the nonparametric estimate based on the resamples is faster than that of the conventional nonparametric estimation method by an order of the number of the resamples. The proposed models and estimation methods are evaluated through using simulated and empirical examples. Both the simulated and empirical examples show that the proposed nonparametric estimation based on resamples outperforms existing estimation methods
1
then proposes some non- and semi-parametric dimension reductions methods to deal with the case where the dimensionality of either the regressors or the summary statistics is large. Meanwhile
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Gao, Jiti
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Hong, Han
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Department of Econometrics and Business Statistics, Monash Business School
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Monash Econometrics and Business Statistics Working Papers
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subject:"nonparametric estimator"
(11 results)
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Nonparametric Regression Approach to Bayesian Estimation
Gao, Jiti
;
Hong, Han
-
Department of Econometrics and Business Statistics, …
-
2014
summary statistic. Both the asymptotic theory and the finite sample study show that the
nonparametric
estimate
of this …
Persistent link: https://www.econbiz.de/10011093868
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