The iteratively reweighted estimating equation in minimum distance problems
From MaRDI portal
Publication:956819
DOI10.1016/S0167-9473(02)00326-2zbMath1400.62074OpenAlexW2068224536MaRDI QIDQ956819
Bruce G. Lindsay, Ayanendranath Basu
Publication date: 26 November 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00326-2
robustnessHellinger distanceNewton-Raphson algorithmiteratively reweighted least squaresdisparityfixed point algorithmiteratively reweighted estimating equation
Related Items (11)
Minimum disparity estimation for continuous models: Efficiency, distributions and robustness ⋮ On the robustness properties for maximum likelihood estimators of parameters in exponential power and generalized T distributions* ⋮ Reliable inference for complex models by discriminative composite likelihood estimation ⋮ Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions ⋮ Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance ⋮ An extension of the Gauss-Newton algorithm for estimation under asymmetric loss ⋮ Minimum disparity estimation: improved efficiency through inlier modification ⋮ Nonparametric and robust methods. (Editorial) ⋮ Minimum disparity computation via the iteratively reweighted least integrated squares algorithms ⋮ A weighted likelihood approach to problems in survival data ⋮ Building and using semiparametric tolerance regions for parametric multinomial models
Cites Work
- Minimum distance density-based estimation
- Comments on a data based bandwidth selector
- Lower bounds for bandwidth selection in density estimation
- Minimum Hellinger distance estimates for parametric models
- Minimum distance estimators
- Efficiency versus robustness: The case for minimum Hellinger distance and related methods
- Minimum negative exponential disparity estimation in parametric models
- The unifying role of iterative generalized least squares in statistical algorithms. With comments by Bent Jørgensen, Peter McCullagh, Joe R. Hill and a rejoinder by the author
- Pathologies of some minimum distance estimators
- The ``automatic robustness of minimum distance functionals
- Minimum disparity estimation for continuous models: Efficiency, distributions and robustness
- Estimation by the minimum distance method
- Robust statistical inference: weighted likelihoods or usual m-estimation?
- Minimum hellinger distance estimation for normal models
- The Minimum Distance Method
- Minimum Hellinger Distance Estimation for Multivariate Location and Covariance
- Robust weighted Cramér-von Mises Estimators of location, with minimax variance in ϵ-contamination neighbourhoods
- Minimum Hellinger Distance Estimation for the Analysis of Count Data
- How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
- On minimum cramer-von mises-norm parameter estimation
- Minimum mean squared estimation of location and scale parameters under misspecification of the model
- Minimum Distance Estimators for Location and Goodness of Fit
- Minimum Distance and Robust Estimation
- Robust regression using iteratively reweighted least-squares
- Weighted Likelihood Equations with Bootstrap Root Search
- Robust predictive distributions for exponential families
- Generalised weighted Cramer-von Mises distance estimators
- The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data
- Estimation by the Minimum Distance Method in Nonparametric Stochastic Difference Equations
- 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
- Unnamed Item
This page was built for publication: The iteratively reweighted estimating equation in minimum distance problems
