Maximum Lq-likelihood Estimation in Functional Measurement Error Models
From MaRDI portal
Publication:5089467
DOI10.5705/ss.202019.0414OpenAlexW3176539106MaRDI QIDQ5089467
Manuel Galea, Patricia Gimenez, Lucas Guarracino
Publication date: 19 July 2022
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.5705/ss.202019.0414
robustnessincidental parametersfunctional measurement error modelsmaximum \(\mathrm{L}_q\)-likelihood
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression
- Fast and robust estimation of the multivariate errors in variables model
- Estimation in a multivariate errors in variables regression model: Large sample results
- Minimum Hellinger distance estimates for parametric models
- Elliptical functional models.
- Convergence behavior of an iterative reweighting algorithm to compute multivariate M-estimates for location and scatter
- Robust weighted orthogonal regression in the errors-in-variables model
- The incidental parameter problem since 1948
- Possible generalization of Boltzmann-Gibbs statistics.
- Comparing consistent estimators in comparative calibration models
- Maximum L\(q\)-likelihood estimation
- Robust inference in a linear functional model with replications using the \(t\) distribution
- Robust estimation in the simple errors-in-variables model
- Corrected score function for errors-in-variables models: Methodology and application to generalized linear models
- On robust estimation via pseudo-additive information
- Standard and robust orthogonal regression
- Measurement Error
- The effects of measurement error on parameter estimation
- A routine for converting regression algorithms into corresponding orthogonal regression algorithms
- Sow aspects of robustness in thr functional errors-in-variables regression model
- Estimation in the presence of incidental parameters
- Robust Hypothesis Testing via Lq-Likelihood
- Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach
- Choosing a robustness tuning parameter
- Measurement Error in Nonlinear Models
- Robust estimation of a linear functional relationship
- Consistent Estimates Based on Partially Consistent Observations
This page was built for publication: Maximum Lq-likelihood Estimation in Functional Measurement Error Models
