Joint estimation of monotone curves via functional principal component analysis
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Publication:2242157
DOI10.1016/J.CSDA.2021.107343OpenAlexW3200208511MaRDI QIDQ2242157
Yei Eun Shin, Yu Ding, Lan Zhou
Publication date: 9 November 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107343
penalizationB-splinesfunctional data analysisspline smoothingmonotone smoothingrelative curvature function
Cites Work
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- A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects
- Principal components analysis of sampled functions
- Simultaneous non-parametric regressions of unbalanced longitudinal data
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Nonparametric kernel regression subject to monotonicity constraints
- Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
- Joint modelling of paired sparse functional data using principal components
- Some Statistical Methods for Comparison of Growth Curves
- Estimating Smooth Monotone Functions
- Order-Preserving Nonparametric Regression, With Applications to Conditional Distribution and Quantile Function Estimation
- Principal component models for sparse functional data
- Nonparametric Regression Analysis of Longitudinal Data
- Principal Modes of Variation for Processes with Continuous Sample Curves
- CALCULATION OF THE SMOOTHING SPLINE WITH WEIGHTED ROUGHNESS MEASURE
- Additive isotone regression
- A Simplex Method for Function Minimization
- Functional Data Analysis for Sparse Longitudinal Data
- Functional data analysis of generalized regression quantiles
- Shape constrained additive models
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