Optimal Rate of Convergence of Monotone Empirical Bayes Tests for a Normal Mean

2001 ◽  
Author(s):  
Shanti S. Gupta ◽  
Jianjun Li
Keyword(s):  
Rate Of Convergence ◽  
Empirical Bayes ◽  
Optimal Rate ◽  
Optimal Rate Of Convergence ◽  
Normal Mean

scholarly journals Davis-type theorems for martingale difference sequences

2005 ◽  
Vol 2005 (2) ◽  
pp. 159-165 ◽  
Author(s):  
George Stoica
Keyword(s):  
Rate Of Convergence ◽  
Classical Case ◽  
Moderate Deviation ◽  
Sharp Contrast ◽  
Optimal Rate ◽  
Martingale Difference ◽  
Normalization Factor ◽  
Second Moment ◽  
Optimal Rate Of Convergence ◽  
Difference Sequences

We study Davis-type theorems on the optimal rate of convergence of moderate deviation probabilities. In the case of martingale difference sequences, under the finite pth moments hypothesis (1≤p<∞), and depending on the normalization factor, our results show that Davis' theorems either hold if and only if p>2 or fail for all p≥1. This is in sharp contrast with the classical case of i.i.d. centered sequences, where both Davis' theorems hold under the finite second moment hypothesis (or less).

scholarly journals ESTIMATING THE QUADRATIC VARIATION SPECTRUM OF NOISY ASSET PRICES USING GENERALIZED FLAT-TOP REALIZED KERNELS

2016 ◽  
Vol 33 (6) ◽  
pp. 1457-1501 ◽  
Author(s):  
Rasmus Tangsgaard Varneskov
Keyword(s):  
Rate Of Convergence ◽  
Asymptotic Variance ◽  
Asymptotic Properties ◽  
Bias Reduction ◽  
Quadratic Variation ◽  
Optimal Rate ◽  
Good Efficiency ◽  
Optimal Rate Of Convergence ◽  
Integrated Variance ◽  
Exogenous Noise

This paper analyzes a generalized class of flat-top realized kernel estimators for the quadratic variation spectrum, that is, the decomposition of quadratic variation into integrated variance and jump variation. The underlying log-price process is contaminated by additive noise, which consists of two orthogonal components to accommodate α-mixing dependent exogenous noise and an asymptotically non-degenerate endogenous correlation structure. In the absence of jumps, the class of estimators is shown to be consistent, asymptotically unbiased, and mixed Gaussian at the optimal rate of convergence, n1/4. Exact bounds on lower-order terms are obtained, and these are used to propose a selection rule for the flat-top shrinkage. Bounds on the optimal bandwidth for noise models of varying complexity are also provided. In theoretical and numerical comparisons with alternative estimators, including the realized kernel, the two-scale realized kernel, and a bias-corrected pre-averaging estimator, the flat-top realized kernel enjoys a higher-order advantage in terms of bias reduction, in addition to good efficiency properties. The analysis is extended to jump-diffusions where the asymptotic properties of a flat-top realized kernel estimate of the total quadratic variation are established. Apart from a larger asymptotic variance, they are similar to the no-jump case. Finally, the estimators are used to design two classes of (medium) blocked realized kernels, which produce consistent, non-negative estimates of integrated variance. The blocked estimators are shown to have no loss either of asymptotic efficiency or in the rate of consistency relative to the flat-top realized kernels when jumps are absent. However, only the medium blocked realized kernels achieve the optimal rate of convergence under the jump alternative.

scholarly journals ADAPTIVE ESTIMATION OF FUNCTIONALS IN NONPARAMETRIC INSTRUMENTAL REGRESSION

2015 ◽  
Vol 32 (3) ◽  
pp. 612-654 ◽  
Author(s):  
Christoph Breunig ◽  
Jan Johannes
Keyword(s):  
Rate Of Convergence ◽  
Adaptive Estimation ◽  
Optimal Choice ◽  
Optimal Rate ◽  
Structural Function ◽  
Regularity Conditions ◽  
Adaptive Estimator ◽  
Optimal Rate Of Convergence ◽  
Pointwise Estimation ◽  
Additional Regularity

We consider the problem of estimating the valueℓ(ϕ) of a linear functional, where the structural functionϕmodels a nonparametric relationship in presence of instrumental variables. We propose a plug-in estimator which is based on a dimension reduction technique and additional thresholding. It is shown that this estimator is consistent and can attain the minimax optimal rate of convergence under additional regularity conditions. This, however, requires an optimal choice of the dimension parametermdepending on certain characteristics of the structural functionϕand the joint distribution of the regressor and the instrument, which are unknown in practice. We propose a fully data driven choice ofmwhich combines model selection and Lepski’s method. We show that the adaptive estimator attains the optimal rate of convergence up to a logarithmic factor. The theory in this paper is illustrated by considering classical smoothness assumptions and we discuss examples such as pointwise estimation or estimation of averages of the structural functionϕ.

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