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中山大学岭南学院高级计量经济学课件（I：Nonparametric Econometrics）CH1 Density Estimation
中山大学岭南学院 高级计量经济学 课件（I：Nonparametric Econometrics） CH1 Density Estimation
2017/6/14
中山大学岭南学院高级计量经济学课件（I：Nonparametric Econometrics）CH1 Density Estimation。
Asymptotic Equivalence of Spectral Density Estimation and Gaussian White Noise
Stationary Gaussian process spectral density Sobolev classes Le Cam distance asymptotic equivalence Whittle likelihood log-periodogram regression nonparametric Gaussian scale model signal in Gaussian white noise
2015/8/25
We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f. Asymptotic equivalence, in the sense of Le Cam’s...
ASYMPTOTIC EQUIVALENCE OF SPECTRAL DENSITY ESTIMATION AND GAUSSIAN WHITE NOISE
ASYMPTOTIC EQUIVALENCE SPECTRAL DENSITY ESTIMATION GAUSSIAN WHITE NOISE
2015/8/25
We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f . Asymptotic equivalence, in the sense of Le Cam’...
The Asymptotic Minimax Constant for Sup-Norm Loss in Nonparametric Density Estimation
Density estimation exact constant optimal recovery uniform norm risk white noise
2015/8/25
We develop the exact constant of the risk asymptotics in the uniform norm for density estimation. This constant has first been found for nonparametric regression and for signal estimation in Gaussian ...
ASYMPTOTIC EQUIVALENCE OF DENSITY ESTIMATION AND GAUSSIAN WHITE NOISE
ASYMPTOTIC EQUIVALENCE DENSITY ESTIMATION GAUSSIAN WHITE NOISE
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
Asymptotic Equivalence of Density Estimation and Gaussian White Noise
Asymptotic Equivalence Density Estimation Gaussian White Noise
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
Density estimation is a commonly used test case for non-parametric estimation
methods. We explore the asymptotic properties of estimators based on thresholding of
empirical wavelet coecients. Minim...
Kernel density estimation via diffusion and the complex exponentials approximation problem
condensed density random matrices parabolic PDE
2012/6/21
A kernel method is proposed to estimate the condensed density of the generalized eigenvalues of pencils of Hankel matrices whose elements have a joint noncentral Gaussian distribution with nonidentica...
SEMIPARAMETRIC DENSITY ESTIMATION FOR TIME SERIES WITH
semiparametric density estimation time series multiplicative adjustment
2011/11/11
In this paper, we extend a class of semiparametric density estimators to time series context. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison sho...
Marginal density estimation for linear processes with seasonal long memory
Confidence band empirical process limit theorem mean integrated squared error
2011/2/25
Some convergence results on the kernel density estimator are proven for a class of linear processes with seasonal effects.
Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators
Non-Parametric Maximum Likelihood Density Estimation Simulation-Based Minimum Distance Estimators
2011/2/22
Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a
parametric model that are based on auxiliary non-parametric maximum likelihood density
estimators are shown to...
Marginal density estimation for linear processes with seasonal long memory
Marginal density estimation linear processes seasonal long memory
2011/1/4
Some convergence results on the kernel density estimator are proven for a class of linear processes with seasonal effects. In particular we extend the results of Ho and Hsing (1996a) and Mielniczuk (1...
We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot...
Bandwidth selection in kernel density estimation: oracle inequalities and adaptive minimax optimality
density estimation kernel estimators Lp–risk oracle inequalities adaptive estimation
2010/11/30
We address the problem of density estimation with L p–loss by selection of kernel estimators. We develop a selection procedure and derive corresponiding L p–risk oracle inequalities. It is shown that ...