[PDF] Top 20 Some Improvement on Convergence Rates of Kernel Density Estimator
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Some Improvement on Convergence Rates of Kernel Density Estimator
... complex kernel density ...reduced kernel density ...reduced estimator and investigate its asymptotic bias, variance and ...second estimator is pro- posed and studied in Section 3 ... See full document
13
Density Estimation in Infinite Dimensional Exponential Families
... Our estimator provides a computationally efficient alternative to maximum likelihood based estimators, which suffer from the computational intractability of the log-partition ...proposed estimator is also ... See full document
59
On the convergence rates of kernel estimator and hazard estimator for widely dependent samples
... to some large sample properties of nonparameter estimate based on WOD ...of estimator of fixed design regression model for WOD ...of kernel density estimator for identically distributed ... See full document
10
Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study
... experimentally adjusted to 4.5. The efficiency of this fast algorithm was tested on several simulations of multimodal densities which are known for being di ffi cult cases in the mean of estimation problem, and the proposed ... See full document
8
On the Berry Esséen bound of frequency polygons for ϕ mixing samples
... has convergence rates similar to those of kernel density estimators and greater than the rate for a ...a kernel density ...of some scholars, and they have de- rived ... See full document
11
A Berry-Esseen Type Bound for the Kernel Density Estimator of Length-Biased Data
... the kernel density estimator of length biased data is investigated ...normal convergence rates, but there is a principal difference between ... See full document
8
Kernel Partial Least Squares for Stationary Data
... probabilistic convergence rates of the kernel partial least squares estimator to the true regression function when the input data are not independent and identically distributed, but rather ... See full document
41
A note on the asymptotic normality of the kernel deconvolution density estimator with logarithmic chi square noise
... volatility density estimator. The problem of estimating volatility density has been gaining increasing interest in econometrics in recent years; see, ...the kernel deconvolution ... See full document
16
A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation
... financial econometrics to describe the evolution of financial returns. Model (23) incorpo- rates popular discrete-time SV models (e.g. Taylor (1982)) and discretized continuous- time SV model which assume the ... See full document
22
From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes
... construct estimator of the parameters based on the minimum Hellin- ger distance ...the density of the invariant distribution of the diffusion process and a nonparametric estimator of this ...admits ... See full document
19
Rates of convergence of some estimators in a class of deconvolution problems
... Key Words and Phrases: Deconvolution, density estimation, mean squared error, measurement error, rates of convergence, uniform convergence.. 1 Work supported by NSF Grant DMS-86-13681A[r] ... See full document
10
The Berry Esséen bounds for kernel density estimator under dependent sample
... the kernel density estimator under association and a negatively associated sample, one can refer to Roussas [] and Liang and Baek [] obtained for asymptotic normal- ity, Wei [] for the ... See full document
13
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
... second-order density derivatives to the ...data density, then computing its derivatives, and finally taking their ...good density estimator does not necessarily mean a good density ... See full document
47
Convergence rates of wavelet density estimation for negatively dependent sample
... variables. Soon afterwards, the above results were extended to the case of negatively de- pendent sequences [7]. Chesneau [4] and Liu [12] also considered density estimation for an NA sample. Kou [11] defined ... See full document
13
Bias reduction in kernel density estimation via Lipschitz condition
... our estimator. Its rates of convergence are true for all bandwidths and sample ...contrast, rates of convergence for higher-order kernels and local polynomial estimators are valid only ... See full document
23
Minimum Density Hyperplanes
... Wolfe (1972) has provided early examples of how standard gradient-based methods can fail to converge to a local optimum when used to minimise nonsmooth functions. In the last decade a new class of nonsmooth optimisation ... See full document
33
Regularized Data-Based Nonparametric Filtration of Stochastic Signals
... for density estimation, was extended to the kernel estimates of density ...of convergence and substantially smaller scatter than in the ... See full document
5
Convergence Rates of Density Estimation in Besov Spaces
... B R for the risk was established by Donoho, Johnstone, Kerkyacharian and Picard (“Density estimation by wavelet thresholding,” The Annals of Statistics, Vol. 24, No. 2, 1996, pp. 508-539). To show the lower bound ... See full document
5
Strong representation results of the Kaplan Meier estimator for censored negatively associated data
... This definition was introduced by Joag-Dev and Proschan []. A statistical test depends greatly on sampling. The random sampling without replacement from a finite population is NA, but is not independent. NA sampling has ... See full document
9
Some New Estimators of Integrated Volatility
... [1] O. E. Barndorff-Nielsen and N. Shephard, “Non-gaussian in—Uhlenbeck-Based Models and Some of Their n Financial Economics (with Discussion),” Journal of the Royal Statistical Society: Series B, Vol. 63, No. 2, ... See full document
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