Weak and Strong Uniform Consistency of the Kernel Estimate of a Density and its Derivatives
TLDR
In this article, the estimation of a density and its derivatives by the kernel method is considered, and uniform consistency properties over the whole real line are studied under certain conditions on the density and on the behavior of the window width which are necessary and sufficient for weak and strong uniform consistency of the estimate of the density derivatives.Abstract:
The estimation of a density and its derivatives by the kernel method is considered. Uniform consistency properties over the whole real line are studied. For suitable kernels and uniformly continuous densities it is shown that the conditions $h \rightarrow 0$ and $(nh)^{-1} \log n \rightarrow 0$ are sufficient for strong uniform consistency of the density estimate, where $n$ is the sample size and $h$ is the "window width." Under certain conditions on the kernel, conditions are found on the density and on the behavior of the window width which are necessary and sufficient for weak and strong uniform consistency of the estimate of the density derivatives. Theorems on the rate of strong and weak consistency are also proved.read more
Citations
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Investigating smooth multiple regression by the method of average derivatives
TL;DR: In this paper, the average derivative estimation (ADE) procedure is proposed to estimate the mean response of a random vector by the estimation of the k vector of average derivatives of the vector x of predictor variables.
Journal ArticleDOI
Weak and strong uniform consistency of kernel regression estimates
Y. P. Mack,Bernard W. Silverman +1 more
TL;DR: In this article, under mild conditions on the window, the bandwidth and the underlying distribution of the bivariate observations, the weak and strong uniform convergence rates on a bounded interval were obtained.
Journal ArticleDOI
Optimal Bandwidth Selection in Nonparametric Regression Function Estimation
TL;DR: In this paper, a bandwidth-selection rule is formulated in terms of cross validation, and under mild assumptions on the kernel and the unknown regression function, it is seen that this rule is asymptotically optimal.
Journal ArticleDOI
Monte Carlo Methods of Inference for Implicit Statistical Models
TL;DR: Methods of inference which can be used for implicit statistical models whose distribution theory is intractable are developed, and the kernel method of probability density estimation is advocated for estimating a log-likelihood from simulations of such a model.
Journal ArticleDOI
Rates of strong uniform consistency for multivariate kernel density estimators
Evarist Giné,Armelle Guillou +1 more
TL;DR: In this article, it was shown that the above sequence of normalized suprema converges a.i.d. to 2d ∞ ∫K 2 (x) d x.
References
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Peter J. Bickel,M. Rosenblatt +1 more
TL;DR: In this paper, the authors consider density estimates of the usual type generated by a weight function and obtain limit theorems for the maximum of the normalized deviation of the estimate from its expected value, and for quadratic norms of the same quantity.
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Estimation of a Probability Density Function and Its Derivatives
TL;DR: In this article, it was shown that for a general class of kernels, the assumption that the distribution of the kernel is uniformly continuous is necessary and sufficient for convergence to the real line.