Penalised spline estimation for generalised partially linear single-index models

DOI10.1007/s11222-016-9639-0zbMath1505.62438OpenAlexW2275075967MaRDI QIDQ76315

Yan Yu, Chaojiang Wu, Yuankun Zhang, Chaojiang Wu, Yuankun Zhang, Yan Yu

Publication date: 18 February 2016

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-016-9639-0

zbMATH Keywords

profile likelihoodlow rank approximationpenalised splinesgeneralised additive modelgeneralised linear model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Generalized linear models (logistic models) (62J12)

Related Items (max. 100)

Bayesian analysis of partially linear, single-index, spatial autoregressive models ⋮ Profile likelihood ratio tests for parameter inferences in generalised single-index models ⋮ Estimation and variable selection for quantile partially linear single-index models ⋮ Semiparametric spatial mixed effects single index models ⋮ Efficient estimation of partially linear tail index models using B‐splines ⋮ Ultra high‐dimensional semiparametric longitudinal data analysis ⋮ Generalized expectile regression with flexible response function ⋮ Semiparametric penalized quadratic inference functions for longitudinal data in ultra-high dimensions ⋮ A Novel Estimation Method in Generalized Single Index Models ⋮ Corporate Probability of Default: A Single-Index Hazard Model Approach ⋮ gplsim ⋮ Generalized additive models with flexible response functions ⋮ Automatic search intervals for the smoothing parameter in penalized splines

Uses Software

• R
• WinBUGS
• SemiPar
• mgcv
• gamair

Cites Work

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• Semiparametric Regression
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• Spline-Based Tests in Survival Analysis
• Measurement Error in Nonlinear Models
• Quadratic Inference Functions for Varying‐Coefficient Models with Longitudinal Data
• Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models

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