Simple fixed-effects inference for complex functional models

Park, So Young; Staicu, Ana‐Maria; Xiao, Luo; Crainiceanu, Ciprian M.

doi:10.1093/biostatistics/kxx026

Cited by 16 publications

(20 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use resampling of the subjects (see also Benko et al (2009); Park et al (2017)) to approximate both the unconditional model-based variance component and the variance of the predicted trajectories. But a rigorous study of the bootstrap techniques is somewhat limited in the functional data analysis.…”

Section: Out-of-sample Predictionmentioning

confidence: 99%

Additive Function-on-Function Regression

Kim

Staicu

Maity

et al. 2018

Journal of Computational and Graphical Statistics

Self Cite

View full text Add to dashboard Cite

We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself, as well as the entire covariate trajectory. We develop a computationally efficient estimation methodology based on a novel combination of spline bases with an eigenbasis to represent the trivariate kernel function. We discuss prediction of a new response trajectory, propose an inference procedure that accounts for total variability in the predicted response curves, and construct pointwise prediction intervals. The estimation/inferential procedure accommodates realistic scenarios, such as correlated error structure as well as sparse and/or irregular designs. We investigate our methodology in finite sample size through simulations and two real data applications. Supplementary Material for this article is available online.

show abstract

Section: Out-of-sample Predictionmentioning

confidence: 99%

Additive Function-on-Function Regression

Kim

Staicu

Maity

et al. 2018

Journal of Computational and Graphical Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The two primary methodologic challenges in fitting FoSR models are estimation of smooth fixed effects and accounting for within-subject correlation. As our interest here is in the estimation of population-level marginal models, we take a a bootstrap procedure for both estimation and inference on fixed effects [ 31 ]. We use cyclic cubic regression splines for estimating

, and a tensor product smooth of marginal cyclic cubic splines and cubic splines for estimating

.…”

Section: Methodsmentioning

confidence: 99%

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

Wrobel

Muschelli

Leroux

2021

Sensors

View full text Add to dashboard Cite

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals’ levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42–78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.

show abstract

“…The estimates O .s, T/ and Ǒ T .s/ are displayed in Figure S1 of the Supporting Information. Using the bootstrap of subjects -based methods of Park et al (2015) and B D 1000 bootstrap samples, we construct 95% joint confidence bands for Ǒ T .s/ (Figure 1). The confidence band contains zero for all s, indicating evidence that a mean model .s, T ij / D 0 .s/ is more appropriate.…”

Section: Simulation Studymentioning

confidence: 99%

Longitudinal functional data analysis

Park

Staicu

2015

Stat

Self Cite

View full text Add to dashboard Cite

We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed to study the dynamic behavior of the underlying process, allows prediction of full future trajectory, and is computationally fast. Theoretical properties of this framework are studied and numerical investigations confirm excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis.

show abstract

Simple fixed-effects inference for complex functional models

Cited by 16 publications

References 58 publications

Additive Function-on-Function Regression

Additive Function-on-Function Regression

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

Longitudinal functional data analysis

Contact Info

Product

Resources

About