Invariance principles for deconvolving kernel density estimation for stationary sequences of random variables
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Publication:1973314
DOI10.1016/S0378-3758(99)00162-7zbMath0952.62030OpenAlexW1978687910WikidataQ126573640 ScholiaQ126573640MaRDI QIDQ1973314
Publication date: 27 April 2000
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0378-3758(99)00162-7
Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)
Related Items (3)
Optimal iterative density deconvolution ⋮ Low Order Approximations in Deconvolution and Regression with Errors in Variables ⋮ Nonparametric deconvolution of density estimation based on observed sums
Cites Work
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- A strong invariance principle for associated sequences
- Weak convergence for weighted empirical processes of dependent sequences
- Adaptively local one-dimensional subproblems with application to a deconvolution problem
- On the optimal rates of convergence for nonparametric deconvolution problems
- Almost sure invariance principles for partial sums of mixing B-valued random variables
- Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes
- Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes
- On the weak convergence of empirical processes in sup-norm metrics
- A functional law of the iterated logarithm for empirical distribution functions of weakly dependent random variables
- Weak convergence of empirical and quantile processes in sup-norm metrics via KMT-constructions
- The functional central limit theorem for strongly mixing processes
- Covariance inequalities for strongly mixing processes
- Strong approximation of the quantile processes and its applications under strong mixing properties
- On the centering of a simple linear rank statistic
- Remarks on Some Nonparametric Estimates of a Density Function
- A consistent nonparametric density estimator for the deconvolution problem
- Deconvolving kernel density estimators
- Optimal Rates of Convergence for Deconvolving a Density
- Gaussian deconvolution via differentiation
- Deconvolution with supersmooth distributions
- Mixing Conditions for Markov Chains
- An almost sure invariance principle for the empirical distribution function of mixing random variables
- Invariance principles for deconvoluting kernel density estimation
- Consistent deconvolution in density estimation
- Reproducing kernel Hilbert spaces and the law of the iterated logarithm for Gaussian processes
- Skorohod embedding of multivariate RV's, and the sample DF
- Heuristic Approach to the Kolmogorov-Smirnov Theorems
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