Methods for Scalar-on-Function Regression

被引：122

作者：

Reiss, Philip T. ^{[1
,2
,3
]}

Goldsmith, Jeff ^{[4
]}

Shang, Han Lin ^{[5
]}

Ogden, R. Todd ^{[4
,6
]}

机构：

[1] NYU, Sch Med, Dept Child & Adolescent Psychiat, New York, NY 10003 USA

[2] NYU, Sch Med, Dept Populat Hlth, New York, NY 10003 USA

[3] Univ Haifa, Dept Stat, Haifa, Israel

[4] Columbia Univ, Mailman Sch Publ Hlth, Dept Stat, New York, NY USA

[5] Australian Natl Univ, Res Sch Finance Actuarial Studies & Stat, Canberra, ACT, Australia

[6] New York State Psychiat Inst & Hosp, New York, NY USA

来源：

INTERNATIONAL STATISTICAL REVIEW | 2017年 / 85卷 / 02期

基金：

美国国家卫生研究院;

关键词：

Functional additive model; functional generalised linear model; functional linear model; functional polynomial regression; functional single-index model; non-parametric functional regression; BAYESIAN BANDWIDTH ESTIMATION; GENERALIZED LINEAR-MODELS; TIME-SERIES PREDICTION; LIKELIHOOD RATIO TESTS; NONPARAMETRIC REGRESSION; ADDITIVE-MODELS; PRINCIPAL; SELECTION; COMPONENTS; DIMENSIONALITY;

D O I：

10.1111/insr.12163

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images and so onare considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorising the basic model types as linear, non-linear and non-parametric. We discuss publicly available software packages and illustrate some of the procedures by application to a functional magnetic resonance imaging data set.

引用

页码：228 / 249

页数：22

共 50 条

[41] Simultaneous variable selection, clustering, and smoothing in function-on-scalar regression
Mehrotra, Suchit
Maity, Arnab
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2022, 50 (01): : 180 - 199
[42] Confounder adjustment in single index function-on-scalar regression model
Ding, Shengxian
Zhou, Xingcai
Lin, Jinguan
Liu, Rongjie
Huang, Chao
ELECTRONIC JOURNAL OF STATISTICS, 2024, 18 (02): : 5679 - 5714
[43] High-Dimensional Spatial Quantile Function-on-Scalar Regression
Zhang, Zhengwu
Wang, Xiao
Kong, Linglong
Zhu, Hongtu
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2022, 117 (539) : 1563 - 1578
[44] Generalized multilevel function-on-scalar regression and principal component analysis
Goldsmith, Jeff
Zipunnikov, Vadim
Schrack, Jennifer
BIOMETRICS, 2015, 71 (02) : 344 - 353
[45] Online robust estimation and bootstrap inference for function-on-scalar regression
Cheng, Guanghui
Hu, Wenjuan
Lin, Ruitao
Wang, Chen
STATISTICS AND COMPUTING, 2025, 35 (01)
[46] INACCURACIES OF MEASURING METHODS AND THEIR INFLUENCE ON THE REGRESSION FUNCTION
SAUER, W
ELECTROCOMPONENT SCIENCE AND TECHNOLOGY, 1984, 11 (03): : 243 - 247
[47] A robust functional partial least squares for scalar-on-multiple-function regression
Beyaztas, Ufuk
Shang, Han Lin
JOURNAL OF CHEMOMETRICS, 2022, 36 (04)
[48] A functional mixed model for scalar on function regression with application to a functional MRI study
Ma, Wanying
Xiao, Luo
Liu, Bowen
Lindquist, Martin A.
BIOSTATISTICS, 2021, 22 (03) : 439 - 454
[49] FUNCTION-ON-SCALAR QUANTILE REGRESSION WITH APPLICATION TO MASS SPECTROMETRY PROTEOMICS DATA
Liu, Yusha
Li, Meng
Morris, Jeffrey S.
ANNALS OF APPLIED STATISTICS, 2020, 14 (02): : 521 - 541
[50] Residual Analysis in Generalized Function-on-Scalar Regression for an HVOF Spraying Process
Kuhnt, Sonja
Rehage, Andre
Becker-Emden, Christina
Tillmann, Wolfgang
Hussong, Birger
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2016, 32 (06) : 2139 - 2150

← 1 2 3 4 5 →