Search results for "Component analysis"
showing 10 items of 562 documents
Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition.
2019
Abstract Background and objective It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm. Methods After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To red…
Outlier analysis and principal component analysis to detect fatigue cracks in waveguides
2009
Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), outlier analysis and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defectsensitive features to perform a multivariate diagnosis of damage. The framework presented in this paper is applied to the de…
Application of principal component analysis and wavelet transform to fatigue crack detection in waveguides
2010
Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper …
Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data
2020
Remote sensing observations, products, and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to analyze and understand the underlying physical dynamics and processes driving the Earth System. Dimensionality reduction methods can work with spatio-temporal data sets and decompose the information efficiently. Principal component analysis (PCA), also known as empirical orthogonal functions (EOFs) in geophysics, has been traditionally used to analyze climatic data. However, when nonlinear feature relations are present, PCA/EOF fails. In this article, we pro…
Rotifer vertical distribution in a strongly stratified lake: a multivariate analysis
1998
The main source of variation of rotifer species distributions in lake Arcas-2, a small karstic lake near Cuenca (Spain), was explored by means of principal components factor (PCA) and canonical correlation (CCA) analyses. PCA was performed using rotifer densities and CCA using rotifer densities plus physical and chemical parameters. Factor 1 of PCA separated summer species from winter-spring species and Factor 2 accounted for the variation in the vertical profile. Three summer species with different food habits (Polyarthra dolichoptera, Hexarthra mira and Asplanchna girodi) were grouped together at the positive end of Factor 1, while Factor 2 separated the two hypolimnetic species (Filinia …
Integrated capital shares
2019
In empirical macroeconomics, inter-dependencies between countries are often analysed using cross-country correlations or graphical investigation of time series. This study shows that applying an alternative methodological approach - identification of common unobservable factors and using them as explanatory variables for country-specific time series - indicates a stronger cross-country integration of functional income distributions than the standard methods. The results vary only little between different samples, where both the country and year coverage change. Moreover, the main findings are not sensitive to the way capital depreciation is taken into account. The primary driving factor see…
Forecasting industry sector default rates through dynamic factor models
2008
In this paper we use a reduced-form model for the analysis of portfolio credit risk. For this purpose, we fit a dynamic factor model to a large data set of default rate proxies and macro-variables for Italy. Multiple step ahead density and probability forecasts are obtained by employing both the direct and indirect methods of prediction together with stochastic simulation of the dynamic factor model. We first find that the direct method is the best performer regarding the out-of-sample projection of financial distressful events. In a second stage of the analysis, we find that reducedform portfolio credit risk measures obtained through the dynamic factor model are lower than those correspond…
Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis
2009
Abstract In this paper we use principal components analysis to obtain vulnerability indicators able to predict financial turmoil. Probit modelling through principal components and also stochastic simulation of a Dynamic Factor model are used to produce the corresponding probability forecasts regarding the currency crisis events affecting a number of East Asian countries during the 1997–1998 period. The principal components model improves upon a number of competing models, in terms of out-of-sample forecasting performance.
Leading indicator properties of US high-yield credit spreads.
2010
Abstract In this paper we examine the out-of-sample forecast performance of high-yield credit spreads for real-time and revised data regarding employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest that the best results come from using only a few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. In particular, for employment and at short-run horizons, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks. Moreover, forecast results based on revised data …
A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
2008
The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest the use of the principal component methodology of Stock and Watson [Stock, J.H., Watson, M.W., 2002. Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147–162] for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard [Harvey, A.C., Ruiz, E., Shephard, N., 1994. Multivariate Stochastic Variance Models. Review of Economic Studies, 61, 247–264]. We provide theoretical and Monte Carlo results on this method and apply it to S&P data.