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Análise multivariada de atributos microbiológicos e químicos do solo em florestas com Araucaria angustifolia; Multivariate analysis of soil microbiological and chemical attributes in forests with Araucaria angustifolia

BARETTA, Dilmar; BARETTA, Carolina Riviera Duarte Maluche; CARDOSO, Elke Jurandy Bran Nogueira
Fonte: Sociedade Brasileira de Ciência do Solo Publicador: Sociedade Brasileira de Ciência do Solo
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.73%
Araucaria angustifolia é uma espécie de árvore ameaçada de extinção no Brasil e pouco se conhece sobre os atributos edáficos dessas florestas. Este estudo foi realizado com o objetivo de identificar diferenças entre áreas com araucária naturais e reflorestadas, com base em atributos microbiológicos e químicos do solo, por meio de métodos multivariados, como a análise canônica discriminante (ACD) e a análise de correlação canônica (ACC). As áreas estudadas incluem: 1. floresta nativa com araucária (NF); 2. reflorestamento de araucária (R); 3. reflorestamento de araucária submetido a incêndio acidental (RF); e 4. pastagem natural com araucárias nativas e ocorrência de incêndio (NPF). Foram selecionadas, ao acaso, quinze árvores de araucária por área e sob a copa de cada uma delas foram retiradas três amostras de solo, em três épocas contrastantes. A ACD foi aplicada aos atributos microbianos: C da biomassa microbiana (CBM), respiração basal (C-CO2) e quociente metabólico (qCO2), enquanto a ACC foi aplicada aos atributos microbianos e químicos do solo [pH (CaCl2), C orgânico total (COT) e teores de P, K, Ca, Mg e (H+Al). Os atributos microbianos e químicos do solo apresentaram alta correlação canônica...

Determinação de zeros na matriz de transferência de sistemas MIMO baseada em análise de correlação.; Determination of transfer matrix zeros from MIMO systems based on correlation analysis.

Massaro, Leandro Cuenca
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 02/06/2014 Português
Relevância na Pesquisa
65.7%
O trabalho tem por objetivo avaliar diferentes métodos para identificar zeros na matriz de transferência de sistemas MIMO e propor um método novo baseado em análise de correlação. Estes métodos são utilizados durante a etapa de pré-identificação, a fim de se obter informações relevantes que possam ser utilizadas para se reduzir o tempo dos experimentos, diminuir a variabilidade dos parâmetros dos modelos e melhorar a eficácia dos modelos remanescentes. Estes métodos são aplicados a sistemas MIMO lineares, com dados coletados em malha aberta e em malha fechada. É avaliado o ganho obtido em relação à capacidade de predição dos modelos, a redução do tempo de identificação e o ganho de desempenho do controlador MPC que utiliza estes modelos. O trabalho conclui que a informação de zeros resulta em melhorias no tempo de identificação e no desempenho do controlador MPC.; This work aims to evaluate different methods to identify zeros in the transfer matrix of MIMO systems and to propose a new method based on correlation analysis. These methods are used during the pre-identification stage in order to identify relevant information that can be used to reduce the duration of the experiment, decrease model parameter variability and improve the accuracy of the remaining models. These methods are applied to MIMO linear systems...

Identificação de parametros de mancal através de analise de correlações; Bearing parameters identification through correlation analysis

Fabio Dalmazzo Sanches
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/07/2008 Português
Relevância na Pesquisa
55.66%
Este trabalho apresenta uma metodologia de identificação dos parâmetros de rigidez e amortecimento dos mancais de rolamento de um sistema mecânico rotativo. O método proposto é baseado na equação matricial de Ljapunov e na representação do sistema na forma de espaço de estados. Através da definição de correlações, inserida na equação matricial, monta-se um estimador que relaciona os parâmetros físicos do sistema com as matrizes de correlações das variáveis medidas no domínio do tempo, não sendo necessário o conhecimento da excitação. O objetivo do trabalho é fazer um estudo teórico da aplicação dessa metodologia de identificação em sistemas reais. São feitas simulações considerando-se um sistema rotativo de vinte graus de liberdade excitado aleatoriamente, caso estático, e por desbalanceamento em diversas freqüências de rotação e de características de mancal. Os resultados numéricos demonstram que o método proposto é robusto e viável, podendo ser aplicado na identificação de uma máquina real.; This work presents an identification methodology of stiffness and damping parameters of rolling bearings in a rotor-bearing system. The proposed method is based on Ljapunov matrix equation and state space representation. Through the definition of correlations used in matrix equation...

Correlation Analysis between Time Series of Precipitation and Soil Moisture under a Mediterranean Climate

Sampaio, Elsa; Lima, Júlio; Veiga, Sandro; Corte-Real, João
Fonte: International Soil Tillage Research Organization Publicador: International Soil Tillage Research Organization
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.75%
Rain-fed agriculture in Mediterranean-type environments relies on seasonal precipitation for agro-forestry production. Irregular precipitation rates (P) do influence the spatial-temporal soil-water (SW) distribution to a “critical soil depth” for a “quick” response of the active roots to tap water following a recent precipitation event. We investigated the statistical correlations between time-series of precipitation and soil-water data at depth using the Canonical Correlation Analysis (CCA) technique. Soil-water was measured in a Cambisol Dystic of an experimental plot, located on a smooth hillside in a non-ploughing Portuguese “montado” agro-forestry ecosystem, in a watershed of representative regional geomorphology and land-use of the Alentejo-region, southern Portugal. Soil-water was measured using three time-domain reflectometry (TDR) moisture-sensors since May 12th (2011) at 10-cm, 30-cm and 50-cm depths. Precipitation was measured with a standard rain-gauge (0.1 mm/tip), ca. 8-km of the TDR´s site. We performed CCA on the time-series of hourly data of P and SW recorded from May, 13th to November, 30th. Firstly, CCA involved P and the three variables (each per depth) for SW, considered all together in the model, and...

Canonical correlation analysis of the characteristics of charcoal from Qualea parviflora Mart.

Protásio,Thiago de Paula; Guimarães Neto,Rosalvo Maciel; Santana,João de Deus Pereira de; Guimarães Júnior,José Benedito; Trugilho,Paulo Fernando
Fonte: UFLA - Universidade Federal de Lavras Publicador: UFLA - Universidade Federal de Lavras
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2014 Português
Relevância na Pesquisa
55.71%
This study aimed to examine the relationships between the characteristics of charcoal from Qualea parviflora Mart. using canonical correlation analysis. Five trees were analyzed in such way that 5-cm thick discs were removed from each tree at the base, DBH (1.30 m), middle and top sections. The wood was carbonized in a muffle furnace at a heating rate of 1.67 °C min-1. A canonical correlation analysis was conducted to investigate the relationships between the group formed by fixed carbon, volatile matter, ash, elemental carbon, hydrogen, nitrogen, sulfur and oxygen levels and a second group formed by the gravimetric yield, higher heating value and relative bulk density of the charcoal. A tendency was noted for high levels of fixed carbon and elemental carbon to be associated to low levels of volatile matter, ash and oxygen and to low gravimetric yield. Fixed carbon and elemental carbon levels had a positive relation to higher heating value and to relative bulk density, whereas volatile matter, ash and oxygen levels had a negative relation to such characteristics. The higher the gravimetric yield from carbonization, the higher the volatile matter, ash and oxygen levels will be in the resulting charcoal.

A canonical correlation analysis of the association between carcass and ham traits in pigs used to produce dry-cured ham

Ventura,Henrique T.; Lopes,Paulo S.; Peloso,José V.; Guimarães,Simone E.F.; Carneiro,Antonio Policarpo S.; Carneiro,Paulo L.S.
Fonte: Sociedade Brasileira de Genética Publicador: Sociedade Brasileira de Genética
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2011 Português
Relevância na Pesquisa
55.66%
The association between carcass and ham traits in a pig population used to produce dry-cured ham was studied using canonical correlation analysis. The carcass traits examined were hot carcass weight (HCW), backfat thickness (BT) and loin depth (LD), and the ham traits studied were gross ham weight (GHW), trimmed ham weight (THW), ham inner layer fat thickness (HIFT), ham outer layer fat thickness (HOFT), pH (pH) and the Göfo value. Carcass and ham traits are not independent. The canonical correlations (r) between the carcass and ham traits at 130 kg were 0.77, 0.24 and 0.20 for the first, second and third canonical pair, respectively, and were all significant (p < 0.01) by the Wilks test. The corresponding canonical correlations between the three canonical variate pairs for the carcass and ham traits at 160 kg were 0.88, 0.42 and 0.14, respectively (p < 0.05 for all, except the third). The correlations between the traits and their canonical variate showed an association among HCW, GHW and THW, and between BT and HOFT. These results indicate that carcass traits should be used to cull pigs that are not suitable for dry-cured ham production.

Signaling pathways of PDZ2 domain: A molecular dynamics Interaction Correlation Analysis

Kong, Yifei; Karplus, Martin
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /01/2009 Português
Relevância na Pesquisa
45.81%
PDZ domains are found in many signaling proteins. One of their functions is to provide scaffolds for forming membrane-associated protein complexes by binding to the carboxyl termini of its partners. PDZ domains are thought to play a signal transduction role by propagating the information that binding has occurred to remote sites. In the current study, a molecular dynamics simulation based approach, referred to an interaction correlation analysis, is applied to the PDZ2 domain to identity the possible signal transduction pathways. A residue correlation matrix is constructed from the interaction energy correlation between all residue pairs obtained from the molecular dynamics simulations. Two continuous interaction pathways, starting at the ligand binding pocket, are identified by a hierarchical clustering analysis of the residue correlation matrix. One pathway is mainly localized at the N terminal side of helix α1 and the adjacent C terminus of loop β1–β2. The other pathway is perpendicular to the central β sheet toward the side of PDZ2 domain opposite to the ligand binding pocket. The present results extend previous studies based on multiple sequence analysis, NMR and molecular dynamics simulations. Importantly, they reveal the energetic origin of the long-range coupling. The PDZ2 results...

Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market

Kenett, Dror Y.; Tumminello, Michele; Madi, Asaf; Gur-Gershgoren, Gitit; Mantegna, Rosario N.; Ben-Jacob, Eshel
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 20/12/2010 Português
Relevância na Pesquisa
45.82%
What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question—the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001–2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis...

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 12/02/2014 Português
Relevância na Pesquisa
45.82%
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

A New Methodology of Spatial Cross-Correlation Analysis

Chen, Yanguang
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 19/05/2015 Português
Relevância na Pesquisa
45.87%
Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation...

Canonical correlation analysis of the characteristics of charcoal from Qualea parviflora Mart.; Análise de correlações canônicas das características do carvão vegetal de Qualea parviflora Mart.

Fonte: UFLA - Universidade Federal de Lavras Publicador: UFLA - Universidade Federal de Lavras
Tipo: Artigo de Revista Científica Formato: text/html
Português
Relevância na Pesquisa
55.71%
This study aimed to examine the relationships between the characteristics of charcoal from Qualea parviflora Mart. using canonical correlation analysis. Five trees were analyzed in such way that 5-cm thick discs were removed from each tree at the base, DBH (1.30 m), middle and top sections. The wood was carbonized in a muffle furnace at a heating rate of 1.67 °C min-1. A canonical correlation analysis was conducted to investigate the relationships between the group formed by fixed carbon, volatile matter, ash, elemental carbon, hydrogen, nitrogen, sulfur and oxygen levels and a second group formed by the gravimetric yield, higher heating value and relative bulk density of the charcoal. A tendency was noted for high levels of fixed carbon and elemental carbon to be associated to low levels of volatile matter, ash and oxygen and to low gravimetric yield. Fixed carbon and elemental carbon levels had a positive relation to higher heating value and to relative bulk density, whereas volatile matter, ash and oxygen levels had a negative relation to such characteristics. The higher the gravimetric yield from carbonization, the higher the volatile matter, ash and oxygen levels will be in the resulting charcoal.

An Approach to Variable Aggregation in Efficiency Analysis

Noncheva, Veska; Mendes, Armando B.; Silva, Emiliana
Fonte: Institute of Information Theories and Applications FOI ITHEA Publicador: Institute of Information Theories and Applications FOI ITHEA
Tipo: Artigo de Revista Científica
Publicado em //2009 Português
Relevância na Pesquisa
55.67%
Conference: The paper is selected from International Conference "Classification, Forecasting, Data Mining" CFDM 2009, Varna, Bulgaria, June-July 2009.; In the nonparametric framework of Data Envelopment Analysis the statistical properties of its estimators have been investigated and only asymptotic results are available. For DEA estimators results of practical use have been proved only for the case of one input and one output. However, in the real world problems the production process is usually well described by many variables. In this paper a machine learning approach to variable aggregation based on Canonical Correlation Analysis is presented. This approach is applied for efficiency estimation of all the farms in Terceira Island of the Azorean archipelago.

Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure

Liu, Wei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/03/2014 Português
Relevância na Pesquisa
55.71%
Blind source separation (BSS) is one of the most important and established research topics in signal processing and many algorithms have been proposed based on different statistical properties of the source signals. For second-order statistics (SOS) based methods, canonical correlation analysis (CCA) has been proved to be an effective solution to the problem. In this work, the CCA approach is generalized to accommodate the case with added white noise and it is then applied to the BSS problem for noisy mixtures. In this approach, the noise component is assumed to be spatially and temporally white, but the variance information of noise is not required. An adaptive blind source extraction algorithm is derived based on this idea and a further extension is proposed by employing a dual-linear predictor structure for blind source extraction (BSE).; Comment: 7 pages and 5 figures. The main aim is to show the inherent relationship between generalised canonical correlation analysis and the dual-linear predictor approach presented in two separate conference papers (references [15] and [16])

Multifractal detrended cross-correlation analysis for two nonstationary signals

Zhou, Wei-Xing
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/03/2008 Português
Relevância na Pesquisa
55.64%
It is ubiquitous in natural and social sciences that two variables, recorded temporally or spatially in a complex system, are cross-correlated and possess multifractal features. We propose a new method called multifractal detrended cross-correlation analysis (MF-DXA) to investigate the multifractal behaviors in the power-law cross-correlations between two records in one or higher dimensions. The method is validated with cross-correlated 1D and 2D binomial measures and multifractal random walks. Application to two financial time series is also illustrated.; Comment: 4 RevTex pages including 6 eps figures

Multifractal Height Cross-Correlation Analysis: A New Method for Analyzing Long-Range Cross-Correlations

Kristoufek, Ladislav
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.71%
We introduce a new method for detection of long-range cross-correlations and multifractality - multifractal height cross-correlation analysis (MF-HXA) - based on scaling of qth order covariances. MF-HXA is a bivariate generalization of the height-height correlation analysis of Barabasi & Vicsek [Barabasi, A.L., Vicsek, T.: Multifractality of self-affine fractals, Physical Review A 44(4), 1991]. The method can be used to analyze long-range cross-correlations and multifractality between two simultaneously recorded series. We illustrate a power of the method on both simulated and real-world time series.; Comment: 6 pages, 4 figures

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.82%
When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using classic detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross-correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multi-scale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross-correlation between crude oil and gold futures by taking into consideration the impact of the US dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the MF-DCCA method fails.; Comment: 7 Latex pages including 3 figures

Efficient Dimensionality Reduction for Canonical Correlation Analysis

Avron, Haim; Boutsidis, Christos; Toledo, Sivan; Zouzias, Anastasios
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.64%
We present a fast algorithm for approximate Canonical Correlation Analysis (CCA). Given a pair of tall-and-thin matrices, the proposed algorithm first employs a randomized dimensionality reduction transform to reduce the size of the input matrices, and then applies any CCA algorithm to the new pair of matrices. The algorithm computes an approximate CCA to the original pair of matrices with provable guarantees, while requiring asymptotically less operations than the state-of-the-art exact algorithms.; Comment: 22 pages. 4 figures. To appear in ICML 2013: The 30th International Conference on Machine Learning

Multifractal Detrended Cross-Correlation Analysis of Sunspot Numbers and River Flow Fluctuations

Hajian, S.; Movahed, M. Sadegh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.83%
We use the Detrended Cross-Correlation Analysis (DCCA) to investigate the influence of sun activity represented by sunspot numbers on one of the climate indicators, specifically rivers, represented by river flow fluctuation for Daugava, Holston, Nolichucky and French Broad rivers. The Multifractal Detrended Cross-Correlation Analysis (MF-DXA) shows that there exist some crossovers in the cross-correlation fluctuation function versus time scale of the river flow and sunspot series. One of these crossovers corresponds to the well-known cycle of solar activity demonstrating a universal property of the mentioned rivers. The scaling exponent given by DCCA for original series at intermediate time scale, $(12-24)\leq s\leq 130$ months, is $\lambda = 1.17\pm0.04$ which is almost similar for all underlying rivers at $1\sigma$confidence interval showing the second universal behavior of river runoffs. To remove the sinusoidal trends embedded in data sets, we apply the Singular Value Decomposition (SVD) method. Our results show that there exists a long-range cross-correlation between the sunspot numbers and the underlying streamflow records. The magnitude of the scaling exponent and the corresponding cross-correlation exponent are $\lambda\in (0.76...

Detrended Cross-Correlation Analysis Consistently Extended to Multifractality

Oświȩcimka, Paweł; Drożdż, Stanisław; Forczek, Marcin; Jadach, Stanisław; Kwapień, Jarosław
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.73%
We propose a novel algorithm - Multifractal Cross-Correlation Analysis (MFCCA) - that constitutes a consistent extension of the Detrended Cross-Correlation Analysis (DCCA) and is able to properly identify and quantify subtle characteristics of multifractal cross-correlations between two time series. Our motivation for introducing this algorithm is that the already existing methods like MF-DXA have at best serious limitations for most of the signals describing complex natural processes and often indicate multifractal cross-correlations when there are none. The principal component of the present extension is proper incorporation of the sign of fluctuations to their generalized moments. Furthermore, we present a broad analysis of the model fractal stochastic processes as well as of the real-world signals and show that MFCCA is a robust and selective tool at the same time, and therefore allows for a reliable quantification of the cross-correlative structure of analyzed processes. In particular, it allows one to identify the boundaries of the multifractal scaling and to analyze a relation between the generalized Hurst exponent and the multifractal scaling parameter $\lambda_q$. This relation provides information about character of potential multifractality in cross-correlations and thus enables a deeper insight into dynamics of the analyzed processes than allowed by any other related method available so far. By using examples of time series from stock market...

Asymptotic study of canonical correlation analysis: from matrix and analytic approach to operator and tensor approach

Fine, Jeanne
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2003 Português
Relevância na Pesquisa
65.8%
Asymptotic study of canonical correlation analysis gives the opportunity to present the different steps of an asymptotic study and to show the interest of an operator and tensor approach of multidimensional asymptotic statistics rather than the classical, matrix and analytic approach. Using the last approach, Anderson (1999) assumes the random vectors to have a normal distribution and the non zero canonical correlation coefficients to be distinct. The new approach we use, Fine (2000), is coordinate-free, distribution-free and permits to have no restriction on the canonical correlation coefficients multiplicity order. Of course, when vectors have a normal distribution and when the non zero canonical correlation coefficients are distinct, it is possible to find again Anderson’s results but we diverge on two of them. In this methodological presentation, we insist on the analysis frame (Dauxois and Pousse, 1976), the sampling model (Dauxois, Fine and Pousse, 1979) and the different mathematical tools (Fine, 1987, Dauxois, Romain and Viguier, 1994) which permit to solve problems encountered in this type of study, and even to obtain asymptotic behavior of the analyses random elements such as principal components and canonical variables.)