Estimating density functions: a constrained maximum likelihood approach*
From MaRDI portal
Publication:4498173
DOI10.1080/10485250008832822zbMath0998.62028OpenAlexW2163227134MaRDI QIDQ4498173
Michael X. Dong, Roger J.-B. Wets
Publication date: 28 November 2002
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485250008832822
consistencyepi-convergenceconstrained maximum likelihood estimationMosco-epi-convergencerho-epi-distance
Related Items
Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail, Fusion of hard and soft information in nonparametric density estimation, Unnamed Item, Variational analysis of constrained M-estimators, Formulation and solution strategies for nonparametric nonlinear stochastic programmes with an application in finance, Nonparametric maximum-likelihood estimation of probability measures: existence and consist\-en\-cy
Cites Work
- Strong consistency of approximate maximum likelihood estimators with applications in nonparametrics
- A geometric algorithm for approximating semicontinuous function
- Asymptotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems
- On the estimation of a probability density function by the maximum penalized likelihood method
- Consistency of two nonparametric maximum penalized likelihood estimators of the probability density function
- Nonparametric maximum likelihood estimation by the method of sieves
- Stability analysis for stochastic programs
- Nonparametric maximum likelihood estimation of probability densities by penalty function methods
- Convergence rate of sieve estimates
- On the asymptotics of constrained \(M\)-estimation
- Epi-consistency in restricted regression models. The case of general convex fitting function
- Semi-Infinite Programming: Theory, Methods, and Applications
- Epi‐consistency of convex stochastic programs
- Unified steerable phase I-phase II method of feasible directions for semi-infinite optimization
- Incorporating support constraints into nonparametric estimators of densities
- On the Convergence in Distribution of Measurable Multifunctions (Random Sets) Normal Integrands, Stochastic Processes and Stochastic Infima
- An elementary proof of the strong law of large numbers
- Asymptotic Behavior of Optimal Solutions in Stochastic Programming
- Constrained estimation: Consistency and asymptotics
- Toward a Reconciliation of the Bayesian and Frequentist Approaches to Point Estimation
- Estimation and testing in constrained covariance component models
- Asymptotic Theory for Solutions in Statistical Estimation and Stochastic Programming
- Semidefinite Programming
- Asymptotic Stochastic Programs
- Nonparametric Roughness Penalties for Probability Densities
- Note on the Consistency of the Maximum Likelihood Estimate
- Set-valued analysis
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item