How Many Iterations are Sufficient for Efficient Semiparametric Estimation?
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Publication:2852630
DOI10.1002/sjos.12005zbMath1364.62077arXiv1009.2111OpenAlexW1601848613MaRDI QIDQ2852630
Publication date: 9 October 2013
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1009.2111
semiparametric modelsNewton-Raphson algorithmgeneralized profile likelihoodhigher order asymptotic efficiency\(k\)-step estimation
Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Functional limit theorems; invariance principles (60F17)
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Cites Work
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- Higher order semiparametric frequentist inference with the profile sampler
- General frequentist properties of the posterior profile distribution
- The penalized profile sampler
- Semiparametric additive isotonic regression
- On asymptotically efficient estimation in semiparametric models
- On adaptive estimation
- Profile likelihood and conditionally parametric models
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Observed information in semi-parametric models
- Penalized quasi-likelihood estimation in partial linear models
- An elementary estimator of the partial linear model
- Efficient estimation for the proportional hazards model with interval censoring
- Least angle regression. (With discussion)
- The cluster bootstrap consistency in generalized estimating equations
- Introduction to empirical processes and semiparametric inference
- Iterative estimating equations: Linear convergence and asymptotic properties
- A Semiparametric Mixture Approach to Case-Control Studies With Errors in Covariables
- The Stochastic Difference Between Econometric Statistics
- Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model
- SOME CONVERGENCE THEORY FOR ITERATIVE ESTIMATION PROCEDURES WITH AN APPLICATION TO SEMIPARAMETRIC ESTIMATION
- Generalized Partially Linear Single-Index Models
- On Profile Likelihood
- Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions
- The Kernel Estimate of a Regression Function in Likelihood-Based Models
- The Profile Sampler
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