Modeling body height in prehistory using a spatio-temporal Bayesian errors-in-variables model
DOI10.1007/s10182-015-0260-xzbMath1443.62536OpenAlexW2197974510MaRDI QIDQ1622075
Publication date: 12 November 2018
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-015-0260-x
tensor product splinesmeasurement errorerrors-in-variablesnonparametric regressionBayesian methodsmisclassificationadditive mixed modelsprehistoric living standard
Nonparametric regression and quantile regression (62G08) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Applications of statistics (62P99)
Uses Software
Cites Work
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- On model expansion, model contraction, identifiability and prior information: two illustrative scenarios involving mismeasured variables
- Nonparametric regression with errors in variables
- PRMLT
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Mean field variational Bayesian inference for nonparametric regression with measurement error
- Inference from iterative simulation using multiple sequences
- Semi-parametric estimation in the nonlinear structural errors-in-variables model
- Generalized structured additive regression based on Bayesian P-splines
- Semiparametric Maximum Likelihood for Measurement Error Model Regression
- Nonparametric Methods for Solving the Berkson Errors-in-Variables Problem
- Measurement Error
- Handbook of Spatial Statistics
- Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem
- Simulation-Extrapolation Estimation in Parametric Measurement Error Models
- The Selection of Prior Distributions by Formal Rules
- Semiparametric Regression
- Bayesian Smoothing and Regression Splines for Measurement Error Problems
- Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
- Regression
- Bayesian Measures of Model Complexity and Fit
- Thin Plate Regression Splines
- Smoothing and forecasting mortality rates
- Gaussian Markov Random Fields
- Nonparametric regression in the presence of measurement error
- P-spline ANOVA-type interaction models for spatio-temporal smoothing
- Non-Parametric Regression Estimation from Data Contaminated by a Mixture of Berkson and Classical Errors
- A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem
- Low‐Rank Scale‐Invariant Tensor Product Smooths for Generalized Additive Mixed Models
- Measurement Error in Nonlinear Models
- Are There Two Regressions?
- Maximum likelihood computations for regression with measurement error.
- Deviance information criteria for missing data models
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