A. Mendez Civieta, J. Goldsmith, Y. Wei

This paper introduces the functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components to the quantile regression framework, obtaining a model that can explain the subject specific quantiles conditional on a set of principal component functions. FQPCA is able to capture shifts on the scale and distribution of the data that may affect the quantiles but may not a ect the mean, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by our motivating example: using the accelerometer data from the National Health and Nutrition Examination Survey (NHANES) we analyze the physical activity level of over 3600 people during one day. The proposed methodology can deal with sparse and irregular time measurements and is evaluated in synthetic data and real data analyses.

Keywords: functional data, quantile regression, principal component analysis, accelerometer data


Ramiro Melendreras Award I
November 7, 2023  11:40 AM
CC4: Room 2

Other papers in the same session

Robust and continuous metric subregularity in the radius paradigm

J. Camacho Moro, M. J. Cánovas Cánovas, M. A. López Cerdá, J. Parra López

El problema general de rutas para un dron con costes dependientes de la carga

P. Segura Martínez, I. Plana Andani, J. M. Sanchis Llopis

Cookie policy

We use cookies in order to be able to identify and authenticate you on the website. They are necessary for the correct functioning of it, and therefore they can not be disabled. If you continue browsing the website, you are agreeing with their acceptance, as well as our Privacy Policy.

Additionally, we use Google Analytics in order to analyze the website traffic. They also use cookies and you can accept or refuse them with the buttons below.

You can read more details about our Cookie Policy and our Privacy Policy.