Time-Varying Additive Models for Longitudinal Data

By Xiaoke Zhang, Byeong U. Park, Jane-ling Wang. 2013

57 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ff3b607f52e134125d9258eb23f746c68dfb2219

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. H. Muller
2. José R. Berrendero
3. Antonio Cuevas
4. Jeng-Min Chiou
5. José L. Torrecilla
6. Jane-ling Wang
7. Byeong U. Park
8. Kyunghee Han

In Statistics and Probability

1. H. Muller
2. Jeng-Min Chiou
3. Jane-ling Wang
4. Byeong U. Park
5. José R. Berrendero
6. José L. Torrecilla
7. Antonio Cuevas
8. Kyunghee Han

Citations in the corpus

• Variable selection in functional data classification: a maxima-hunting proposal (2013, methodology): “Other common dimension reduction methods in the functional setting include sliced inverse regression (Hsing and Ren (2009); Jiang, Yu and Wang (2013)) and additive models (Zhang, Park and Wang (2013)).”
• Additive Functional Regression for Densities as Responses (2020, result): “This is in contrast to the backfitting equations studied in Zhang, Park, and Wang (2013), where the corresponding integral operators depend on u.”
• Functional Data Analysis (2016, methodology): “Modeling additive components that are bivariate functions of time and a covariate (Zhang, Park & Wang 2013), this restriction can be avoided.”