Functional principal components analysis by choice of norm
Journal of Multivariate Analysis
Forecast comparison of principal component regression and principal covariate regression
Computational Statistics & Data Analysis
Functional PLS logit regression model
Computational Statistics & Data Analysis
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Computational considerations in functional principal component analysis
Computational Statistics
An overview to modelling functional data
Computational Statistics
Modelling the mean of a doubly stochastic Poisson process by functional data analysis
Computational Statistics & Data Analysis
Distance-based local linear regression for functional predictors
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The relationship between time evolution of stress and flares in Systemic Lupus Erythematosus patients has recently been studied. Daily stress data can be considered as observations of a single variable for a subject, carried out repeatedly at different time points (functional data). In this study, we propose a functional logistic regression model with the aim of predicting the probability of lupus flare (binary response variable) from a functional predictor variable (stress level). This method differs from the classical approach, in which longitudinal data are considered as observations of different correlated variables. The estimation of this functional model may be inaccurate due to multicollinearity, and so a principal component based solution is proposed. In addition, a new interpretation is made of the parameter function of the model, which enables the relationship between the response and the predictor variables to be evaluated. Finally, the results provided by different logit approaches (functional and longitudinal) are compared, using a sample of Lupus patients.