Donsker-type theorems for nonparametric maximum likelihood estimators
DOI10.1007/s00440-006-0031-4zbMath1113.60028OpenAlexW2150631298MaRDI QIDQ880938
Publication date: 21 May 2007
Published in: Probability Theory and Related Fields (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00440-006-0031-4
Convolution productsDifferentiable functionalsNonparametric maximum likelihood estimatorPlug-in propertyUniform central limit theorem
Asymptotic properties of parametric estimators (62F12) Density estimation (62G07) Central limit and other weak theorems (60F05) Topological linear spaces of test functions, distributions and ultradistributions (46F05)
Related Items (19)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency
- On maximum likelihood estimation in infinite dimensional parameter spaces
- Empirical processes indexed by Lipschitz functions
- Estimation of integrated squared density derivatives
- Optimal rates of convergence for nonparametric estimators
- Geometrizing rates of convergence. II
- Invariant tests for uniformity on compact Riemannian manifolds based on Sobolev norms
- Rates of convergence for minimum contrast estimators
- Estimation of integral functionals of a density and its derivatives
- Necessary and sufficient conditions for weak convergence of smoothed empirical processes.
- Nonparametric estimators which can be ``plugged-in.
- Efficient estimation of integral functionals of a density
- Estimation of integral functionals of a density
- Probability inequalities for likelihood ratios and convergence rates of sieve MLEs
- Weak convergence and empirical processes. With applications to statistics
- Hellinger-consistency of certain nonparametric maximum likelihood estimators
- Bracketing metric entropy rates and empirical central limit theorems for function classes of Besov- and Sobolev-type
- On local \(U\)-statistic processes and the estimation of densities of functions of several sample variables
- PERFORMANCE LIMITS FOR ESTIMATORS OF THE RISK OR DISTRIBUTION OF SHRINKAGE-TYPE ESTIMATORS, AND SOME GENERAL LOWER RISK-BOUND RESULTS
- Empirical and Gaussian processes on Besov classes
- Asymptotically minimax estimation of concave and convex distribution functions
- Uniform Central Limit Theorems
- Estimating Densities of Functions of Observations
- Rootnconsistent density estimators for sums of independent random variables
- Real Analysis and Probability
- On the Asymptotic Distribution of Differentiable Statistical Functions
This page was built for publication: Donsker-type theorems for nonparametric maximum likelihood estimators