Search results for "ESTIMATOR"
showing 10 items of 313 documents
Investigating the level of significance of the coherence function in cardiovascular variability analysis
2002
Although the presence of significant coupling between cardiovascular variability series is usually verified according to the threshold value of 0.5 in the coherence function (CF), spectral estimator parameters should also be considered. In this study, the surrogate data technique was introduced to define the level of significance of the CF. The proposed method determined a frequency-dependent threshold over which the hypothesis of zero coherence was rejected. The weighted covariance method and the autoregressive method were used to estimate the CF on simulated series with different degrees of linear coupling and on real cardiovascular data. The threshold was dependent on the type and parame…
Comparative Study of the a Posteriori Error Estimators for the Stokes Problem
2007
The research presented is focused on a comparative study of a posteriori error estimation methods to various approximations of the Stokes problem. Mainly, we are interested in the performance of functional type a posterior error estimates and their comparison with other methods. We show that functional type a posteriori error estimators are applicable to various types of approximations (including non-Galerkin ones) and robust with respect to the mesh structure, type of the finite element and computational procedure used. This allows the construction of effective mesh adaptation procedures in all cases considered. Numerical tests justify the approach suggested.
Neural Networks as Soft Sensors: a Comparison in a Real World Application.
2006
Physical atmosphere parameters, as temperature or humidity, can be indirectly estimated on the surface of a monument by means of soft sensors based on neural networks, if an ambient air monitoring station works in the neighborhood of the monument itself. Since the soft sensors work as virtual instruments, the accuracy of such measurements has to be analyzed and validated from statistical and metrological points of view. The paper compares different typologies of neural networks, which can be used as soft sensors in a complex real world application: a non invasive monitoring of the conservation state of old monuments. In this context, several designed connessionistic systems, based on radial…
Low-frequency noise inδ13C andδ18O tree ring data: A case study ofPinus uncinatain the Spanish Pyrenees
2010
[1] Isotopic discrimination measurements in tree rings are becoming increasingly important estimators of past environmental change. Potential biases inherent to these parameters, including age trend and level offset are, however, not well understood. We here perform measurements on a new millennium-long data set of decadally resolved δ18O and δ13C discrimination from 25 high-elevation pine trees in the Spanish Pyrenees to investigate whether such low-frequency biases exist and how they alter the long-term behavior of derived time series. Alignment of the tree ring data by biological age reveals age trends over the first one to four centuries after germination. On average, isotope values cha…
Autocorrelation Metrics to Estimate Soil Moisture Persistence From Satellite Time Series: Application to Semiarid Regions
2021
Satellite-derived soil moisture (SM) products have become an important information source for the study of land surface processes in hydrology and land monitoring. Characterizing and estimating soil memory and persistence from satellite observations is of paramount relevance, and has deep implications in ecology, water management, and climate modeling. In this work, we address the problem of SM persistence estimation from microwave sensors using several autocorrelation metrics that, unlike traditional approaches, build on accurate estimates of the autocorrelation function from nonuniformly sampled time series. We show how the choice of the autocorrelation estimator can have a dramatic impac…
Alternative Diagonality Criteria for SOBI
2015
Blind source separation (BSS) is a multivariate data analysis method, whose roots are in the signal processing community. BSS is applied in diverse fields, including, for example, brain imaging and economic time series analysis. In the BSS model there are interesting latent uncorrelated variables, and the aim is to estimate the latent variables from multiple linear combinations of them. In this article we assume that these variables are weakly stationary time series, and we consider estimation methods which are based on approximate joint diagonalization of autocovariance matrices. In the popular SOBI estimator, a set of matrices is most diagonal when the sum of squares of their diagonal ele…
Aplicación del Estimador de Parámetros de Segmentación por Media-desplazada (EPSM) a las imágenes de satélite de muy alta resolución espacial: Tetuán…
2015
<p>La segmentación de imágenes constituye un paso crucial en el Análisis de Imágenes Basado en Objetos (AIBO). Combinando distintos valores de los parámetros de entrada de los algoritmos de segmentación se obtienen diferentes resultados. En general, los parámetros óptimos seleccionados se determinan mediante interpretación visual; por lo tanto, la definición de las combinaciones óptimas es una tarea considerablemente difícil. En la presente investigación, se propone una herramienta analítica que denominamos Estimador de Parámetros de Segmentación por Media-desplazada (EPSM) aplicada a la selección automatizada de los valores de los parámetros de segmentación en las imágenes de satélit…
MODELLING USER UNCERTAINTY FOR DISCLOSURE RISK AND DATA UTILITY
2002
In this paper we show how a simple model that captures user uncertainty can be used to define suitable measures of disclosure risk and data utility. The model generalizes previous results of Duncan and Lambert.1 We present several examples to illustrate how the new measures can be used to implement existing optimality criteria for the choice of the best form of data release.
Practical Issues on Energy-Growth Nexus Data and Variable Selection With Bayesian Analysis
2018
Abstract Given that the energy-growth nexus (EGN) is short of a complete theoretical base, the production function used therein is typically complemented with numerous variables that characterize an economy. Researchers are often puzzled not only with the selection of variables per se, but also with the variable sources and the various data handlings which become apparent and available only after years of experience in this research field. Thus, this chapter is divided into two distinctive parts: The first part contains an overview of the available data sources for the EGN as well as a succinct selection of advice on data handlings, transformations, and interpretations that could come handy…
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
2013
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-013-0424-x There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their maximum likelihood counterparts. The success of LA-…