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Desenvolvimento de tecnicas quimiometricas de compressão de dados e deredução de ruido instrumental aplicadas a oleo diesel e madeira de eucalipto usando espectroscopia NIR; Development of chemometric technics for data compression and reduction of diesel oil and eucalypus wood employing NIR spectroscopy

Heronides Adonias Dantas Filho
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 16/03/2007 Português
Relevância na Pesquisa
46.90043%
Neste trabalho foram desenvolvidas e aplicadas técnicas de seleção de amostras e de variáveis espectrais para calibração multivariada a partir do Algoritmo das Projeções Sucessivas (APS). Também foi utilizada a transformada wavelet para resolver problemas de redução de ruído associado a dados de espectroscopia NIR (Infravermelho Próximo), na construção de modelos de calibração multivariada baseados em Regressão Linear Múltipla (MLR) para estimativa de parâmetros de qualidade de óleo diesel combustível e também de madeira de eucalipto. Os espectros NIR de transmitância para óleo diesel e de reflectância para madeira de eucalipto foram registrados empregando-se um equipamento NIR-Bomem com detector de Arseneto de Gálio e Índio. Para a aplicação em óleo diesel, foram estudadas as regiões espectrais: 850 - 1.100 nm, 1.100 - 1.570 nm e 1.570 - 2.500 nm. Para as amostras de madeira de eucalipto foi empregada a região de 1.100 - 2.500 nm. Os resultados do uso de técnicas de seleção de variáveis e amostras por MLR comprovaram sua simplicidade frente os modelos de regressão por mínimos quadrados parciais (PLS) que empregam toda a região espectral e transformação em variáveis latentes e são mais complexos de interpretar. O emprego de seleção de amostras demonstrou ainda a possibilidade de procedimentos de recalibrações e transferência de calibração que utilizam um número reduzido de amostras...

Assessment of the performance of eight filtering algorithms by using full-waveform LiDAR data of unmanaged eucalypt forest

Gonçalves, G.; Gomes Pereira, L.
Fonte: FeLis/ University of Freiburg Publicador: FeLis/ University of Freiburg
Tipo: Conferência ou Objeto de Conferência
Português
Relevância na Pesquisa
37.340198%
In this study the strengths and weaknesses of eight filtering algorithms are evaluated by using the mean, standard deviation and RMSE metrics. Seven of these algorithms are implemented in the freeware software ALDPAT (Airborne LiDAR Data Processing and Analysis Tools) and the eighth, known as the Axelsson filter, in the commercial software Terrascan. The referred metrics are calculated by using DTM of topographic surfaces with quite different morphologies and vegetation covers. Forty-three of these surfaces, on circular plots of 400 m2 each, are covered by brushwood and unmanaged eucalypt forest with different stand characteristics. The mean tree density is around 1600 trees per hectare. The reference DTM for assessing the DTM produced by filtering full-waveform LiDAR data using the eight filtering algorithms are created with the help of a total station and geodetic GNSS receivers. The results show that the Axelsson and the so-called Polynomial Two Surface Fitting filters give the best results in terms of RMSE. Nonetheless, the results also show that all the tested filters are suitable for the filtering of full-waveform LiDAR data used in forestry related work, and collected over areas with great amount and high brushwood, chaotic eucalypt tree distribution and high tree density. The results obtained for a forest area with such characteristics – among which it should be mentioned a RMSE of 15 cm - are quite surprising.

On the cloud deployment of a session abstraction for service/data aggregation

Domingos, João Nuno Silva Tabar
Fonte: Faculdade de Ciências e Tecnologia Publicador: Faculdade de Ciências e Tecnologia
Tipo: Dissertação de Mestrado
Publicado em //2013 Português
Relevância na Pesquisa
46.626084%
Dissertação para obtenção do Grau de Mestre em Engenharia Informática; The global cyber-infrastructure comprehends a growing number of resources, spanning over several abstraction layers. These resources, which can include wireless sensor devices or mobile networks, share common requirements such as richer inter-connection capabilities and increasing data consumption demands. Additionally, the service model is now widely spread, supporting the development and execution of distributed applications. In this context, new challenges are emerging around the “big data” topic. These challenges include service access optimizations, such as data-access context sharing, more efficient data filtering/ aggregation mechanisms, and adaptable service access models that can respond to context changes. The service access characteristics can be aggregated to capture specific interaction models. Moreover, ubiquitous service access is a growing requirement, particularly regarding mobile clients such as tablets and smartphones. The Session concept aggregates the service access characteristics, creating specific interaction models, which can then be re-used in similar contexts. Existing Session abstraction implementations also allow dynamic reconfigurations of these interaction models...

Experimental Investigation of Angular Stackgram Filtering for Noise Reduction of SPECT Projection Data: Study with Linear and Nonlinear Filters

Happonen, Antti P.; Koskinen, Matti O.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
37.375168%
We discuss data filtering prior to image reconstruction. For this kind of filtering, the radial direction of the sinogram is routinely employed. Recently, we have introduced an alternative approach to sinogram data processing, exploiting the angular information in a novel way. This new stackgram representation can be regarded as an intermediate form of the sinogram and image domains. In this experimental study, we compare the radial sinogram and angular stackgram filtering methods using physical SPECT phantoms. Our study is carried out by employing simple linear and nonlinear filters with ten different Gaussian kernels, in order to provide a comparable investigation. According to our results, angular stackgram filtering with the nonlinear filters provides the best resolution-noise tradeoff of the compared methods. Besides, stackgram filtering with these filters seems to preserve the resolution in an exceptional way. Visually, noise in the reconstructed images after stackgram filtering appears more “powdery” in comparison with radial sinogram filtering.

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

Bashir, Ali Kashif; Lim, Se-Jung; Hussain, Chauhdary Sajjad; Park, Myong-Soon
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
Publicado em 06/07/2011 Português
Relevância na Pesquisa
47.1647%
RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.

The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors

Patel, Zubin H.; Kottyan, Leah C.; Lazaro, Sara; Williams, Marc S.; Ledbetter, David H.; Tromp, hbGerard; Rupert, Andrew; Kohram, Mojtaba; Wagner, Michael; Husami, Ammar; Qian, Yaping; Valencia, C. Alexander; Zhang, Kejian; Hostetter, Margaret K.; Harley,
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 12/02/2014 Português
Relevância na Pesquisa
47.31157%
Next Generation Sequencing studies generate a large quantity of genetic data in a relatively cost and time efficient manner and provide an unprecedented opportunity to identify candidate causative variants that lead to disease phenotypes. A challenge to these studies is the generation of sequencing artifacts by current technologies. To identify and characterize the properties that distinguish false positive variants from true variants, we sequenced a child and both parents (one trio) using DNA isolated from three sources (blood, buccal cells, and saliva). The trio strategy allowed us to identify variants in the proband that could not have been inherited from the parents (Mendelian errors) and would most likely indicate sequencing artifacts. Quality control measurements were examined and three measurements were found to identify the greatest number of Mendelian errors. These included read depth, genotype quality score, and alternate allele ratio. Filtering the variants on these measurements removed ~95% of the Mendelian errors while retaining 80% of the called variants. These filters were applied independently. After filtering, the concordance between identical samples isolated from different sources was 99.99% as compared to 87% before filtering. This high concordance suggests that different sources of DNA can be used in trio studies without affecting the ability to identify causative polymorphisms. To facilitate analysis of next generation sequencing data...

A Generalized Adaptive Mathematical Morphological Filter for LIDAR Data

Cui, Zheng
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Português
Relevância na Pesquisa
47.25331%
Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth’s surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in “cut-off” errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain...

Sinogram bow-tie filtering in FBP PET reconstruction

Abella, Mónica; Vaquero, Juan José; Soto-Montenegro, M. L.; Lage, E.; Desco, Manuel
Fonte: American Association of Physicists in Medicine Publicador: American Association of Physicists in Medicine
Tipo: info:eu-repo/semantics/acceptedVersion; info:eu-repo/semantics/article Formato: application/pdf
Publicado em /05/2009 Português
Relevância na Pesquisa
37.246646%
Low-pass filtering of sinograms in the radial direction is the most common practice to limit noise amplification in filtered back projection FBP reconstruction of positron emission tomography studies. Other filtering strategies have been proposed to prevent the loss in resolution due to low-pass radial filters, although results have been diverse. Using the well-known properties of the Fourier transform of a sinogram, the authors defined a binary mask that matches the expected shape of the support region in the Fourier domain of the sinogram “bow tie”. This mask was smoothed by a convolution with a ten-point Gaussian kernel which not only avoids ringing but also introduces a pre-emphasis at low frequencies. A new filtering scheme for FBP is proposed, comprising this smoothed bow-tie filter combined with a standard radial filter and an axial filter. The authors compared the performance of the bow-tie filtering scheme with that of other previously reported methods: Standard radial filtering, angular filtering, and stackgram-domain filtering. All the quantitative data in the comparisons refer to a baseline reconstruction using a ramp filter only. When using the smallest size of the Gaussian kernel in the stackgram domain, the authors achieved a noise reduction of 33% at the cost of degrading radial and tangential resolutions 14.5% and 16%...

Massively Scalable Data Warehouses with Performance Predictability

Costa, João Pedro Matos da
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
37.367778%
Data Warehouses (DW) são ferramentas fundamentais no apoio ao processo de tomada de decisão, e que lidam com grandes volumes de dados cada vez maiores, que normalmente são armazenados usando o modelo em estrela (star schema). No entanto, o resultado das pesquisas e análises deve estar disponível em tempo útil. Contudo, como a complexidade das pesquisas que são submetidas é cada vez maior, com padrões de pesquisa imprevisíveis (ad-hoc), e devido ao aumento do número de pesquisas que são submetidas e executadas simultaneamente, provoca que o tempo de execução das pesquisas seja imprevisível. Mercados concorrenciais requerem que os resultados sejam disponibilizados em tempo útil para ajudar o processo de tomada de decisão. Isto não é apenas uma questão de obter resultados rápidos, mas de garantir que os resultados estarão disponíveis antes das decisões serem tomadas. Estratégias de pré-computação de pesquisas podem ajudar na obtenção de resultados mais rápidos, no entanto a sua utilização é limitada apenas a pesquisas com padrões conhecidos (planeados). Contudo, as consultas com padrões de pesquisa imprevisíveis (ad-hoc) são executadas sem quaisquer garantias de execução de tempo. São vários os fatores que influenciam a capacidade da DW fornecer resultados às pesquisas em tempo útil...

Asychronous [i.e. asynchronous] data fusion for AUV navigation using extended Kalman filtering.; Asynchronous data fusion for AUV navigation using extended Kalman filtering

Thorne, Richard L.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xi, 154 p.;28 cm.
Português
Relevância na Pesquisa
37.233005%
A truly Autonomous Vehicle must be able to determine its global position in the absence of external transmitting devices. This requires the optimal integration of all available organic vehicle attitude and velocity sensors. This thesis investigates the extended Kalman filtering method to merge asynchronous heading, heading rate, velocity, and DGPS information to produce a single state vector. Different complexities of Kalman filters, with biases and currents, are investigated with data from Florida Atlantic's Ocean Explorer II surface run. This thesis used a simulated loss of DGPS data to represent the vehicle's submergence. All levels of complexity of the Kalman filters are shown to be much more accurate then the basic dead reckoning solution commonly used aboard autonomous underwater vehicles.; NA; NA

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

Bakhtiar, Qutub A
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Português
Relevância na Pesquisa
37.407983%
With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally...

To Believe or Not To Believe: Improving Distributed Data Fusion with Second Order Knowledge

Marchetti, Luca
Fonte: La Sapienza Universidade de Roma Publicador: La Sapienza Universidade de Roma
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
37.268667%
Most of works on Distributed Data Fusion (DDF ) systems investigate how to optimize or improve the fusion process by optimistically assuming the correctness of uncertainty models. The impact of using poor-quality information is not well addressed. Moreover, only a few works address how to improve the overall quality of the estimation result. The aim of this thesis is to cover this aspect of Multi-Agent Systems and give an improvement on designing DDF frameworks, by using second order knowledge. In particular, we show that it is possible to improve the effectiveness of the estimation by using information not related only to the underlying filtering process. Understanding and reasoning about the context wherein the fusion process is performed is a key point for providing better and reliable estimations of the environment. Information about the intrinsic characteristics of the environment, the relations among fused data and the interactions among objects, all of them influence the performance of Data Fusion applications. The main result of this thesis is a complete framework for information source selection and integration, by using the reliability of sources as second order knowledge. Specifically, we developed a Multi-Agent Multi-Object Data Fusion system and applied it to the problem of Cooperative Robot Tag game and Soccer Robots. We address the question of reliability and existence of prior knowledge that impacts on the quality of data fusion process applied in such contexts. The chosen scenarios are well suited to test the effectiveness of our proposal: the dynamic component of these test-beds...

Data Quality of Fleet Management Systems in Open Pit Mining: Issues and Impacts on Key Performance Indicators for Haul Truck Fleets

Hsu, Nick
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
37.298281%
Open pit mining operations typically rely upon data from a Fleet Management Systems (FMS) in order to calculate Key Performance Indicators (KPI’s). For production and maintenance planning and reporting purposes, these KPI’s typically include Mechanical Availability, Physical Availability, Utilization, Production Utilization, Effective Utilization, and Capital Effectiveness, as well as Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR). This thesis examined the datasets from FMS’s from two different software vendors. For each FMS, haul truck fleet data from a separate mine site was analyzed. Both mine sites had similar haul trucks, and similar fleet sizes. From a qualitative perspective, it was observed that inconsistent labelling (assignment) of activities to time categories is a major impediment to FMS data quality. From a quantitative perspective, it was observed that the datasets from both FMS vendors contained a surprisingly high proportion of very short duration states, which are indicative of either data corruption (software / hardware issues) or human error (operator input issues) – which further compromised data quality. In addition, the datasets exhibited a mismatch (i.e. lack of one-to-one correspondence) between Repair events and Unscheduled Maintenance Down Time states...

Combining Genetic Algorithms, Neural Networks and Data Filtering for Time Series Forecasting

Neves, José; Cortez, Paulo
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em /10/1998 Português
Relevância na Pesquisa
46.79147%
In the last few decades an increasing focus as been put over the field of Time Series Forecasting (TSF), the forecast of a time ordered variable. Contributions from the arenas of Operational Research, Statistics, and Computer Science as lead to solid TSF methods (eg. Exponential Smoothing or Regression) that replaced the old fashion ones, which were primary based on intuition. Although these methods give accurate forecasts on linear Time Series (TS), their handicap is with noise or nonlinear components, which is a commum situation (eg. in financial daily TS). An alternative approach for TSF as recently emerged from the field of Artificial Intelligence, where new optimization algorithms, such as Genetic Algorithms and Artificial Neural Networks have became popular. Following this trend, the present work reports on a Genetic Algoritm Neural Network system, and in its use for TSF.

Application of feedback connection artificial neural network to seismic data filtering

Djarfour, Noureddine; Aifa, Tahar; Baddari, Kamel; Mihoubi, Abdelhafid; Ferahtia, Jalal
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/11/2008 Português
Relevância na Pesquisa
46.92855%
The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection that characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by a back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both an iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data shows that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data sets.

Constrained Function Based En-Route Filtering for Sensor Networks

Yu, Chia-Mu; Lu, Chun-Shien; Kuo, Sy-Yen
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/11/2009 Português
Relevância na Pesquisa
37.231235%
Sensor networks are vulnerable to \emph{false data injection attack} and \emph{path-based DoS} (PDoS) attack. While conventional authentication schemes are insufficient for solving these security conflicts, an \emph{en-route filtering} scheme acts as a defense against these two attacks. To construct an efficient en-route filtering scheme, this paper first presents a Constrained Function based message Authentication (CFA) scheme, which can be thought of as a hash function directly supporting the en-route filtering functionality. Together with the \emph{redundancy property} of sensor networks, which means that an event can be simultaneously observed by multiple sensor nodes, the devised CFA scheme is used to construct a CFA-based en-route filtering (CFAEF) scheme. In contrast to most of the existing methods, which rely on complicated security associations among sensor nodes, our design, which directly exploits an en-route filtering hash function, appears to be novel. We examine the CFA and CFAEF schemes from both the theoretical and numerical aspects to demonstrate their efficiency and effectiveness.; Comment: 26 pages, single column, extension from a preliminary version appeared in IEEE WCNC 2009

Evaluating Optical Fiber Links with Data Filtering and Allan Deviation

Calosso, Claudio Eligio; Clivati, Cecilia; Micalizio, Salvatore
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/07/2015 Português
Relevância na Pesquisa
56.92855%
In this paper we propose a simple method to reject the high-frequency noise in the evaluation of statistical uncertainty of coherent optical fiber links. Specifically, we propose a preliminary data filtering, separated from the frequency stability computation. In this way, it is possible to use the Allan deviation as estimator of stability, to get unbiased data, which are representative of the noise process affecting the delivered signal. Our approach is alternative to the use of the modified Allan deviation, which is largely adopted in this field. We apply this processing to the experimental data we obtained on a 1284 km coherent optical link for frequency dissemination, which we realized in Italy. We also show how the so-called Lambda-type commercial phase/frequency counters can be used to this purpose.

Median Filtering is Equivalent to Sorting

Suomela, Jukka
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/06/2014 Português
Relevância na Pesquisa
37.274138%
This work shows that the following problems are equivalent, both in theory and in practice: - median filtering: given an $n$-element vector, compute the sliding window median with window size $k$, - piecewise sorting: given an $n$-element vector, divide it in $n/k$ blocks of length $k$ and sort each block. By prior work, median filtering is known to be at least as hard as piecewise sorting: with a single median filter operation we can sort $\Theta(n/k)$ blocks of length $\Theta(k)$. The present work shows that median filtering is also as easy as piecewise sorting: we can do median filtering with one piecewise sorting operation and linear-time postprocessing. In particular, median filtering can directly benefit from the vast literature on sorting algorithms---for example, adaptive sorting algorithms imply adaptive median filtering algorithms. The reduction is very efficient in practice---for random inputs the performance of the new sorting-based algorithm is on a par with the fastest heap-based algorithms, and for benign data distributions it typically outperforms prior algorithms. The key technical idea is that we can represent the sliding window with a pair of sorted doubly-linked lists: we delete items from one list and add items to the other list. Deletions are easy; additions can be done efficiently if we reverse the time twice: First we construct the full list and delete the items in the reverse order. Then we undo each deletion with Knuth's dancing links technique.; Comment: 1 + 24 pages...

Possible high-temperature superconductors predicted from electronic structure and data-filtering algorithms

Klintenberg, M.; Eriksson, O.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/09/2011 Português
Relevância na Pesquisa
46.85892%
We report here the completion of the electronic structure of the majority of the known stoichiometric inorganic compounds, as listed in the International Crystal Structure Data-base (ICSD). We make a detailed comparison of the electronic structure, crystal geometry and chemical bonding of cuprate high temperature superconductors, with the calculated over sixty thousand electronic structures. Based on compelling similarities of the electronic structures in the normal state and a data-filtering technique, we propose that high temperature superconductivity is possible for electron- or hole-doping in a much larger group of materials than previously considered. The indentified materials are composed of over one hundred layered compounds, most which hitherto are untested with respect to their super conducting properties. Of particular interest are the following materials; Ca$_2$(CuBr$_2$O$_2$), K$_2$CoF$_4$, Sr$_2$(MoO$_4$) and Sr$_4$V$_3$O$_{10}$, which are discussed in detail.

Kullback-Leibler distance as a measure of the information filtered from multivariate data

Tumminello, Michele; Lillo, Fabrizio; Mantegna, Rosario Nunzio
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2007 Português
Relevância na Pesquisa
37.233005%
We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation matrix from a reference model and we show that the expected values are known also when the specific model is unknown. We propose to make use of the Kullback-Leibler distance to estimate the information extracted from a correlation matrix by correlation filtering procedures. We also show how to use this distance to measure the stability of filtering procedures with respect to statistical uncertainty. We explain the effectiveness of our method by comparing four filtering procedures, two of them being based on spectral analysis and the other two on hierarchical clustering. We compare these techniques as applied both to simulations of factor models and empirical data. We investigate the ability of these filtering procedures in recovering the correlation matrix of models from simulations. We discuss such an ability in terms of both the heterogeneity of model parameters and the length of data series. We also show that the two spectral techniques are typically more informative about the sample correlation matrix than techniques based on hierarchical clustering...