NONLINEAR WAVELET DENSITY ESTIMATION FOR TRUNCATED AND DEPENDENT OBSERVATIONS

2011 ◽  
Vol 09 (04) ◽  
pp. 587-609 ◽  
Author(s):  
JUAN-JUAN CAI ◽  
HAN-YING LIANG
Keyword(s):  
Asymptotic Expression ◽  
Kernel Estimator ◽  
Density Estimator ◽  
Mean Integrated Squared Error ◽  
Wavelet Estimator ◽  
Dependent Observations ◽  
Squared Error ◽  
Integrated Squared Error ◽  
Wavelet Density Estimation ◽  
Mixing Sequence

In this paper, we provide an asymptotic expression for mean integrated squared error (MISE) of nonlinear wavelet density estimator for a truncation model. It is assumed that the lifetime observations form a stationary α-mixing sequence. Unlike for kernel estimator, the MISE expression of the nonlinear wavelet estimator is not affected by the presence of discontinuities in the curves. Also, we establish asymptotic normality of the nonlinear wavelet estimator.

NONLINEAR WAVELET ESTIMATION OF CONDITIONAL DENSITY UNDER LEFT-TRUNCATED AND α-MIXING ASSUMPTIONS

2011 ◽  
Vol 09 (06) ◽  
pp. 989-1023 ◽  
Author(s):  
SI-LI NIU ◽  
HAN-YING LIANG
Keyword(s):  
Asymptotic Expression ◽  
Kernel Estimator ◽  
Conditional Density ◽  
Left Truncation ◽  
Mean Integrated Squared Error ◽  
Wavelet Estimator ◽  
Squared Error ◽  
Integrated Squared Error ◽  
The Mean ◽  
Mixing Sequence

In this paper, we construct a nonlinear wavelet estimator of conditional density function for a left truncation model. We provide an asymptotic expression for the mean integrated squared error (MISE) of the estimator. It is assumed that the lifetime observations form a stationary α-mixing sequence. Unlike for kernel estimator, the MISE expression of the nonlinear wavelet estimator is not affected by the presence of discontinuities in the curves.

scholarly journals A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator underα-Mixing Dependence

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Christophe Chesneau
Keyword(s):  
Density Estimation ◽  
General Setting ◽  
Type Model ◽  
Hard Thresholding ◽  
Mean Integrated Squared Error ◽  
Wavelet Estimator ◽  
Squared Error ◽  
Integrated Squared Error ◽  
The Mean ◽  
Weakly Dependent Data

We consider the estimation of an unknown functionffor weakly dependent data (α-mixing) in a general setting. Our contribution is theoretical: we prove that a hard thresholding wavelet estimator attains a sharp rate of convergence under the mean integrated squared error (MISE) over Besov balls without imposing too restrictive assumptions on the model. Applications are given for two types of inverse problems: the deconvolution density estimation and the density estimation in a GARCH-type model, both improve existing results in this dependent context. Another application concerns the regression model with random design.

scholarly journals Density and Risk Function of the Circular Kernel Study

2019 ◽  
Vol 12 (4) ◽  
pp. 1612-1642
Author(s):  
Didier Alain Njamen Njomen ◽  
Hubert Clovis Yayebga
Keyword(s):  
Necessary Conditions ◽  
Asymptotic Expression ◽  
Survival Function ◽  
Risk Function ◽  
Kernel Estimator ◽  
Mean Square ◽  
Squared Error ◽  
Integrated Squared Error ◽  
The Mean ◽  
Optimal Window

This article is based on the works of [1], [2] and [3] on the estimation of the survival function and the function of risk in independent cases and identically distributed with and without censorship from which we established the bias and variance of the density of the circular kernel. In addition, we determined the optimal window b ∗ n of this estimator after having first established the mean square error (MSE) and mean integrated squared error (MISE) which are necessary conditions for obtaining the optimal window. Finally, we have established the asymptotic expression of the bias of the risk function of the circular kernel estimator

scholarly journals Pointwise Optimality of Wavelet Density Estimation for Negatively Associated Biased Sample

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 176 ◽  
Author(s):  
Renyu Ye ◽  
Xinsheng Liu ◽  
Yuncai Yu
Keyword(s):  
Density Estimation ◽  
Mean Squared Error ◽  
Density Estimator ◽  
Wavelet Method ◽  
Wavelet Estimator ◽  
Negatively Associated ◽  
Squared Error ◽  
Wavelet Density Estimation ◽  
The Mean ◽  
Block Thresholding

This paper focuses on the density estimation problem that occurs when the sample is negatively associated and biased. We constructed a block thresholding wavelet estimator to recover the density function from the negatively associated biased sample. The pointwise optimality of this wavelet density estimation is shown as L p ( 1 ≤ p < ∞ ) risks over Besov space. To validate the effectiveness of the block thresholding wavelet method, we provide some examples and implement the numerical simulations. The results indicate that our block thresholding wavelet density estimator is superior in terms of the mean squared error (MSE) when comparing with the nonlinear wavelet density estimator.

scholarly journals Transmetric Density Estimation

2016 ◽  
Vol 5 (2) ◽  
pp. 35
Author(s):  
Sigve Hovda
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Convergence Order ◽  
Graphical Display ◽  
Mean Integrated Squared Error ◽  
Squared Error ◽  
Integrated Squared Error ◽  
The Mean ◽  
Convergence Orders

<div>A transmetric is a generalization of a metric that is tailored to properties needed in kernel density estimation.  Using transmetrics in kernel density estimation is an intuitive way to make assumptions on the kernel of the distribution to improve convergence orders and to reduce the number of dimensions in the graphical display.  This framework is required for discussing the estimators that are suggested by Hovda (2014).</div><div> </div><div>Asymptotic arguments for the bias and the mean integrated squared error is difficult in the general case, but some results are given when the transmetric is of the type defined in Hovda (2014).  An important contribution of this paper is that the convergence order can be as high as $4/5$, regardless of the number of dimensions.</div>

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