Search results for "multivariate statistic"
showing 10 items of 327 documents
Clustering and Registration of Multidimensional Functional Data
2013
In order to find similarity between multidimensional curves, we consider the application of a procedure that provides a simultaneous assignation to clusters and alignment of such functions. In particular we look for clusters of multivariate seismic waveforms based on EM-type procedure and functional data analysis tools.
Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects
2014
Purpose/Objective(s) To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variatio…
Comparing Spatial and Spatio-temporal FPCA to Impute Large Continuous Gaps in Space
2018
Multivariate spatio-temporal data analysis methods usually assume fairly complete data, while a number of gaps often occur along time or in space. In air quality data long gaps may be due to instrument malfunctions; moreover, not all the pollutants of interest are measured in all the monitoring stations of a network. In literature, many statistical methods have been proposed for imputing short sequences of missing values, but most of them are not valid when the fraction of missing values is high. Furthermore, the limitation of the methods commonly used consists in exploiting temporal only, or spatial only, correlation of the data. The objective of this paper is to provide an approach based …
Functional principal component analysis for multivariate multidimensional environmental data
2015
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in modelling these data has been generated, but the complexity of spatio-temporal models, together with the size of the dataset, results in a challenging task. The modelization is even more complex in presence of multivariate data. Since some modelling problems are more natural to think through in functional terms, even if only a finite number of observations is available, treating the data as functional can be useful (Berrendero et al. in Comput Stat Data Anal 55:2619–2634, 2011). Although in Ramsay and Silverman (Functional data analysis, 2nd edn. Springer, New York, 2005) the case of multiva…
Multivariate versus univariate calibration for nonlinear chemiluminescence data
2001
Abstract Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested. The use of a double logarithmic transformatio…
Lithosphere Structure, Heat Flow, Gravity, and Other Geoparameters in Central Europe
1991
To clarify structure and evolution of Central European lithosphere, an approach is taken which combines several methods: multivariate statistical data analysis and modelling of data sets, as geology, crustal structure, seismic travel times, heat flow, gravity, geoid heights, topography, vertical motions and unloading affects.
Reporting heterogeneity in health: an extended latent class approach
2012
This article explores how individual socio-economic characteristics affect unobserved heterogeneity in self-reporting behaviour and health production using a multivariate finite mixture model. Results show a positive relationship between objective and subjective observable health indicators and true health and support the existence of self-reporting bias related to socio-economic characteristics and individual life styles.
Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in southern Sicily
2008
A certain number of studies have been carried out in recent years that aim at developing and applying a model capable of assessing water erosion of soil. Some of these have tried to quantitatively evaluate the volumes of soil loss, while others have focused their efforts on the recognition of the areas most prone to water erosion processes. This article presents the results of a research whose objective was that of evaluating water erosion susceptibility in a Sicilian watershed: the Naro river basin. A geomorphological study was carried out to recognize the water erosion landforms and define a set of parameters expressing both the intensity of hydraulic forces and the resistance of rocks/so…
Multi-year drought frequency analysis at multiple sites by operational hydrology - A comparison of methods
2006
Abstract This paper compares two generators of yearly water availabilities from sources located at multiple sites with regard to their ability to reproduce the characteristics of historical critical periods and to provide reliable results in terms of the return period of critical sequences of different length. The two models are a novel multi-site Markov mixture model explicitly accounting for drought occurrences and a multivariate ARMA. In the case of the multisite Markov mixture model parameter estimation is limited to a search in the parameter space guided by the value of parameter λ to show the sensitivity of the model to this parameter. Application to two of the longest time series of …
Three-dimensional object-distortion-tolerant recognition for integral imaging using independent component analysis
2009
Independent component analysis (ICA) aims at extracting unknown components from multivariate data assuming that the underlying components are mutually independent. This technique has been successfully applied to the recognition and classification of objects. We present a method that combines the benefits of ICA and the ability of the integral imaging technique to obtain 3D information for the recognition of 3D objects with different orientations. Our recognition is also possible when the 3D objects are partially occluded by intermediate objects.