Semiparametric regression with shape-constrained penalized splines

DOI10.1016/j.csda.2011.04.018zbMath1218.62032OpenAlexW1965489797MaRDI QIDQ553513

Martin L. Hazelton, Turlach, Berwin A.

Publication date: 27 July 2011

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.04.018

zbMATH Keywords

MCMClinear mixed modeltruncated multivariate normal

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Linear regression; mixed models (62J05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Testing constancy in monotone response models, Unimodal regression using Bernstein–Schoenberg splines and penalties, Fast and flexible methods for monotone polynomial fitting, Bayesian estimation on semiparametric models with shape restricted B-splines, Gaussian process modeling with inequality constraints, A flexible sequential Monte Carlo algorithm for parametric constrained regression, Bayesian regression on non-parametric mixed-effect models with shape-restricted Bernstein polynomials, Revisiting fitting monotone polynomials to data, A dual active set algorithm for optimal sparse convex regression, Fitting monotone polynomials in mixed effects models, Spline estimation of generalised monotonic regression

Uses Software

• R
• SemiPar
• pyuvdata

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Inference using shape-restricted regression splines
• Fast simulation of truncated Gaussian distributions
• A dual algorithm for convex-concave data smoothing by cubic \(C^ 2\)- splines
• Constrained \(L_ p\) approximation
• Constrained interpolation and smoothing
• Smoothing noisy data under monotonicity constraints existence, characterization and convergence rates
• An algorithm for cubic spline fitting with convexity constraints
• Isotonic, convex and related splines
• Direct generalized additive modeling with penalized likelihood.
• Isotonic smoothing splines under sequential designs
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• On the degrees of freedom in shape-restricted regression.
• Fitting monotonic polynomials to data
• Generalized structured additive regression based on Bayesian P-splines
• Shape constrained smoothing using smoothing splines
• Bayesian Isotonic Regression and Trend Analysis
• A Bayesian Approach to Non-Parametric Monotone Function Estimation
• The Nonparametric Estimation of Branching Curves
• Estimating Smooth Monotone Functions
• Monotone B-Spline Smoothing
• Semiparametric Estimation of Monotone and Concave Utility Functions for Polychotomous Choice Models
• Semiparametric Regression
• Penalized regression with model-based penalties
• Inequality-Constrained Multivariate Smoothing Splines with Application to the Estimation of Posterior Probabilities
• Smoothing Splines and Shape Restrictions
• Generalized monotonic regression based on B-splines with an application to air pollution data
• Empirical Bayes Gibbs sampling
• Point Estimates of Ordinates of Concave Functions