Search results for "multivariate statistic"

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.

On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water

2001

Abstract Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …

Multivariate SPC of a sequencing batch reactor for wastewater treatment

2007

Data from a sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal from wastewater have been analysed in order to propose an efficient MSPC scheme of the process. Different multivariate bilinear approaches have been applied and compared in terms of their capabilities for on-line and off-line fault detection and diagnosis. The typical three-way data structure from a batch process was unfolded batch-wise and variable-wise. In the latter case, two models were built: with (AT) and without (WKFH) removing the main non-linear behaviour of the process data. Since the process consists of several stages, the monitoring strategies tested include: one model for all stages a…

Les déterminants de la réussite scolaire à l'école primaire au Brésil: une analyse multifactorielle

2016

RESUMEN: Una problemática educativa actual en Brasil es el alto porcentaje de alumnos en la escolaridad primaria que muestran un bajo nivel académico. Esta situación justifica el análisis de los determinantes del rendimiento académico en matemáticas para la totalidad de alumnos y su comparación al colectivo con un rendimiento considerado insuficiente. Para ello, se emplea una aproximación lineal y multivariante a partir de los datos contenidos en la base de datos del Sistema de Avaliação da Educação Básica 2005. En conjunto, los resultados obtenidos permiten afirmar que las dimensiones asociadas a la educabilidad resultan determinantes para comprender los rendimientos observados en la educa…

Stochastic Approximation for Multivariate and Functional Median

2010

We propose a very simple algorithm in order to estimate the geometric median, also called spatial median, of multivariate (Small (1990)) or functional data (Gervini (2008)) when the sample size is large. A simple and fast iterative approach based on the Robbins-Monro algorithm (Duflo (1997)) as well as its averaged version (Polyak and Juditsky (1992)) are shown to be effective for large samples of high dimension data. They are very fast and only require O(Nd) elementary operations, where N is the sample size and d is the dimension of data. The averaged approach is shown to be more effective and less sensitive to the tuning parameter. The ability of this new estimator to estimate accurately …

Data Banks and Multivariate Statistics in Physical Anthropology

1984

In recent decades, the fields of administration and economy, the press and - last but not least - the sciences have been characterized by an “explosion of knowledge”, and, as a consequence, by the problem of managing the rapidly increasing mass of information. It has been estimated that knowledge doubles each five years, and even that the interval of doubling seems to decrease. The main response to this challenge are computerized and structured data collections called data banks. “Data banks are systems of data collections which are organized according to logical and/or formal criteria; they should make it possible to reproduce the data of the total collection arranged according to differen…