Bayesian regression on non-parametric mixed-effect models with shape-restricted Bernstein polynomials
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Publication:5138187
DOI10.1080/02664763.2016.1142940OpenAlexW2336670295MaRDI QIDQ5138187
Zhong-Zhan Zhang, Jianhua Ding
Publication date: 3 December 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2016.1142940
Uses Software
Cites Work
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- Inference using shape-restricted regression splines
- A simple nonparametric estimator of a strictly monotone regression function
- Semiparametric regression with shape-constrained penalized splines
- Fast simulation of truncated Gaussian distributions
- On the degree approximation of functions in \(C_1[0,1\) by the operators of Meyer-König and Zeller]
- Nonparametric curve estimation with Bernstein estimates
- A general projection framework for constrained smoothing.
- Nonparametric kernel regression subject to monotonicity constraints
- Shape preserving representations and optimality of the Bernstein basis
- A nonparametric Bayes method for isotonic regression
- Shape restricted nonparametric regression with Bernstein polynomials
- Optimal smoothing in nonparametric mixed-effect models
- Bayesian Isotonic Regression and Trend Analysis
- A Bayesian Approach to Non-Parametric Monotone Function Estimation
- Monotone B-Spline Smoothing
- Semiparametric Regression
- Varying-coefficient models and basis function approximations for the analysis of repeated measurements
- Bayesian Measures of Model Complexity and Fit
- Generalization of Bernstein's polynomials to the infinite interval
- A variable selection approach to monotonic regression with Bernstein polynomials
- Estimating a Convex Function in Nonparametric Regression
- Point Estimates of Ordinates of Concave Functions
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