Search results for "Multivariate statistics"
showing 10 items of 290 documents
A multivariate approach to the study of orichalcum ingots from the underwater Gela's archaeological site
2017
Abstract In this work a careful ICP-OES and ICP-MS investigation of 38 ancient ingots has been performed to determine both major components and trace elements content to find a correlation between the observed different features and the composition. The ingots, recovered in an underwater archaeological site of various finds near Gela (CL, Italy), were previously investigated by X-Ray Fluorescence (XRF) spectroscopy to know the composition of the alloy and it was found that the major elements were copper and zinc, in a ratio compatible with the famous orichalcum similar to the contemporary brass that was considered a precious metal in ancient times. The discovery of huge amount this alloy is…
Capillary electrophoresis enhanced by automatic two-way background correction using cubic smoothing splines and multivariate data analysis applied to…
2005
Mixtures of the surfactant classes coconut diethanolamide, cocamido propyl betaine and alkylbenzene sulfonate were separated by capillary electrophoresis in several media containing organic solvents and anionic solvophobic agents. Good resolution between both the surfactant classes and the homologues within the classes was achieved in a BGE containing 80 mM borate buffer of pH 8.5, 20% n-propanol and 40 mM sodium deoxycholate. Full resolution, assistance in peak assignment to the classes (including the recognition of solutes not belonging to the classes), and improvement of the signal-to-noise ratio was achieved by multivariate data analysis of the time-wavelength electropherograms. Cubic s…
Towards unsupervised analysis of second-order chromatographic data: automated selection of number of components in multivariate curve-resolution meth…
2007
A method to apply multivariate curve-resolution unattendedly is presented. The algorithm is suitable to perform deconvolution of two-way data (e.g. retrieving the individual elution profiles and spectra of co-eluting compounds from signals obtained from a chromatograph equipped with multiple-channel detection: LC-DAD or GC-MS). The method is especially adequate to achieve the advantages of deconvolution approaches when huge amounts of data are present and manual application of multivariate techniques is too time-consuming. The philosophy of the algorithm is to mimic the reactions of an expert user when applying the orthogonal projection approach--multivariate curve-resolution techniques. Ba…
Decoupling factors on the energy–output linkage: The Spanish case
2007
The recent increase of energy intensity in Spain and the ratification of the Kyoto protocol call for the implementation of energy policies in Spain. In this paper, we investigate the relationship between Gross Domestic Product (GDP) and Energy Consumption (EC) by taking into account several decoupling factors that can affect this linkage. Specifically, we have considered the temporal aggregation of data and its seasonal adjustments, the multivariate methodology, the substitution between EC and other inputs and the technological changes. Empirical tests reveal a long-run relationship between EC and GDP that can only be established in a complete way with a multivariate cointegration analysis.…
Assessing Frequency Domain Causality in Cardiovascular Time Series with Instantaneous Interactions
2009
Summary Background: The partial directed coherence (PDC) is commonly used to assess in the frequency domain the existence of causal relations between two time series measured in conjunction with a set of other time series. Although the multivariate autoregressive (MVAR) model traditionally used for PDC computation accounts only for lagged effects, instantaneous effects cannot be neglected in the analysis of cardiovascular time series. Objectives: We propose the utilization of an extended MVAR model for PDC computation, in order to improve the evaluation of frequency domain causality in the presence of zero-lag correlations among multivariate time series. Methods: A procedure for the identif…
Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.
2019
Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…
MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series
2014
We present a new time series analysis toolbox, developed in Matlab, for the estimation of the Transfer entropy (TE) between time series taken from a multivariate dataset. The main feature of the toolbox is its fully multivariate implementation, that is made possible by the design of an approach for the non-uniform embedding (NUE) of the observed time series. The toolbox is equipped with parametric (linear) and non-parametric (based on binning or nearest neighbors) entropy estimators. All these estimators, implemented using the NUE approach in comparison with the classical approach based on uniform embedding, are tested on RR interval, systolic pressure and respiration variability series mea…
Seeing Multivariate Data
2011
Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*
2020
The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …
Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data
2016
Air pollution data sets are usually spatio-temporal multivariate data related to time series of different pollutants recorded by a monitoring network. To improve the estimate of functional data when missing values, and mainly long gaps, are present in the original data set, some procedures are here proposed considering jointly Functional Data Analysis and Empirical Orthogonal Function approaches. In order to compare and validate the proposed procedures, a simulation plan is carried out and some performance indicators are computed. The obtained results show that one of the proposed procedures works better than the others, providing a better reconstruction especially in presence of long gaps.