Search results for "Functional Data Analysis"
showing 10 items of 30 documents
Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps
2013
The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and…
Clusters of effects curves in quantile regression models
2018
In this paper, we propose a new method for finding similarity of effects based on quantile regression models. Clustering of effects curves (CEC) techniques are applied to quantile regression coefficients, which are one-to-one functions of the order of the quantile. We adopt the quantile regression coefficients modeling (QRCM) framework to describe the functional form of the coefficient functions by means of parametric models. The proposed method can be utilized to cluster the effect of covariates with a univariate response variable, or to cluster a multivariate outcome. We report simulation results, comparing our approach with the existing techniques. The idea of combining CEC with QRCM per…
Assessing the Beneficial Effects of Economic Growth: The Harmonic Growth Index
2011
In this paper we introduce the multidimensional notion of harmonic growth as a situation of diffused well-being associated to an increase of per capita GDP. We say that a country experienced a harmonic growth if during the observed period all the key indicators, proxies of the endogenous and exogenous forces driving population well-being, show a significantly common pattern with the income dynamics. The notion is operationalized via an index of time series harmony which follows the functional data analysis approach. This Harmonic Growth Index (HGI) is based on comparisons between the coefficients from cubic B-splines interpolation. Such indices are then synthesized in order to provide the g…
Local characteristics of functional marked point processes with applications to seismic data
2022
We present a family of local inhomogeneous mark-weighted summary statistics for general marked point processes. These capture various types of local dependence structures depending on the specified involved weight function. We use them to propose a local random labeling test. This procedure enables us to identify points and thus regions where the random labeling assumption does not hold, for example, when the (functional) marks are spatially dependent. We further present an application to a seismic point pattern with functional marks provided by seismic waveforms. Indeed, despite the relatively long history of point process theory, few approaches to analyzing spatial point patterns where th…
Functional Data Analysis for ECG Recordings of Paroxysmal Atrial Fibrillation Patients Before and After Pulmonary Vein Isolation
2018
Pulmonary vein isolation is the cornestone of current ablation techniques for patients with paroxysmal atrial fibrillation in order avoid recurrences of the arrhythmia and maintain sinus rhythm. This study aimed to analyse the existence of significant variations in surface ECG after pulmonary vein isolation by means of functional data analysis. 12 consecutive unselected patients suffering from paroxysmal atrial fibrillation who underwent catheter ablation were included in the study. Each patient was monitored in sinus rhythm before and after catheter ablation. P-waves of bipolar lead II were delineated. Functional data were fitted from these segments and the first and second derivatives eva…
Misalignment of Spectral Data: Constrained Optimization in a Functional Data Analysis Framework
2022
Across several branches of sciences, a large number of applications involves data represented as functions and curves, for which functional data analysis can play a central role in solving a variety of problem formulations. With some thecnologies, the obtained data are spectra containing a vast amount of information concerning the composition of a sample: in order to infer the chemical composition of the materials from spectra, functional data analysis offers a valuable mean for characterizing the spectral response through identification of peaks position and intensity. The collection of data from different measurement may exhibit similar peak pattern but display misalignment in their peaks…
Common functional component modelling
2005
Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data.
Missing Data in Space-time: Long Gaps Imputation Based On Functional Data Analysis
2017
High dimensional data with spatio-temporal structures are of great interest in many elds of research, but their exhibited complexity leads to practical issues when formulating statistical models. Functional data analysis through smoothing methods is a proper framework for incorporating space-time structures: extending the basic methodology to the multivariate spatio-temporal setting, we refer to Generalized Additive Models for estimating functional data taking the spatial and temporal dependences into account, and to Functional Principal Component Analysis as a classical dimension reduction technique to cope with the high dimensionality and with the number of estimated eects. Since spatial …
Detecting clusters in spatially correlated waveforms
2017
Seismic networks often record signals characterized by similar shapes that provide important information according to their geographic positions. We propose an approach to identify homogeneous clusters of seismic waves, combining analysis of waveforms with metadata and spectrogram information. In waveforms clustering, cross-correlation measures between signals may presents some limitations, so we refer to more recent contributes relating data-depth based clustering analysis. The mechanism for alignment is also an important topic of the analysis: warping (or aligning) procedures identify nuisance effects in phase variation, that, if ignored, may result in a possible loss of information and t…
An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach
2013
The purpose of this study is to examine the development of Italian tourist areas ( circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an…