Search results for "Principal component"
showing 10 items of 514 documents
REGEOTOP: New climatic data fields for East Asia based on localized relief information and geostatistical methods
2004
Climate data fields represent essential tools for climate, biogeographical and agricultural research to run models and to provide observational data for the verification of global climate models (GCM). Climate data fields are generated through interpolation of observations taken at meteorological stations. Most current interpolation procedures try to describe the influence of topography on spatial climatic variations by relating them directly to absolute elevation or by introducing simple relief variables such as exposure. In both cases this may not properly describe spatial climatic variations, particularly not those of precipitation. This paper describes a regionalization procedure (REGEO…
An Analysis of Regional and Intra-annual Precipitation Variability over Iran using Multivariate Statistical Methods
1998
The temporal and spatial precipitation regime of Iran was analysed using multivariate analyses of monthly mean precipitation records for 71 stations. A Principal Component Analysis was applied to the correlation matrix in order to describe the intra-annual variations of precipitation. The Principal Component scores were mapped to visualize the spatial structure of the three derived precipitation regimes. By applying an agglomerative clustering (WARD) of the three Principal Component scores, five homogeneous spatial clusters, representing five precipitation regions, were developed. The intra-annual types of precipitation distribution, shown by the five clusters, are described and discussed.
Trends of mean temperatures and warm extremes in northern tropical Africa (1961-2014) from observed and PPCA-reconstructed time series
2016
Trends in daily maximum (TX) and minimum (TN) temperatures and indices of warm extremes are studied in tropical North Africa, west of the eastern African highlands, from 1961 to 2014. The analysis is based on the concatenation and cross-checking of two observed databases. Due to the large number of missing entries (~25%), a statistical infilling using probabilistic principal component analysis was applied. Averaged over 90 stations, the linear trends of annual mean TX and TN equal respectively +0.021 °C/yr and +0.028 °C/yr. The frequency of very hot days (TX > 35°C) and tropical nights (TN > 20°C), as well as the frequency of daily TX and TN above the 90th percentile (p90) (“warm days” and …
PCA analysis of wind direction climate in the baltic states
2021
Wind direction is one of the fundamental parameters of weather. In this study we investigate the wind direction climate 10 m above surface level in the Baltic States (Estonia, Latvia, Lithuania). The analysis of wind direction over larger regions is usually hindered by the fact that wind direction is a circular variable, which means that averaged values are meaningless. Here we show how Principal Component Analysis (PCA) can be applied to give a large scale overview of typical wind direction patterns in the region. Here we apply PCA to both observational and reanalysis data. The most significant wind direction patterns are detected in both synoptic scale and mesoscale, and we attempt to lin…
Analysis of the authors’ rights collection frontier using PCA-MDEA: an application to the Valencia region
2002
The aim of this paper is to estimate the authors’ rights collection frontier within the collection zones into which the Valencia Region (Spain) has been divided. To be more exact, a nonparametric frontier technique (Modified Data Envelopment Analysis, MDEA) and the Principal Components Analysis (PCA) are jointly employed to map out an initial approach to the potential authors’ rights collection within this region (as divided into collection zones). The analysis has been carried out both jointly and by majority sectors (performing, musical, and audio-visual arts). Publicaciones Econcult: Área de Investigación en Economía de la Cultura y Turismo. Universitat de València
Detection of steering direction using EEG recordings based on sample entropy and time-frequency analysis.
2016
Monitoring driver's intentions beforehand is an ambitious aim, which will bring a huge impact on the society by preventing traffic accidents. Hence, in this preliminary study we recorded high resolution electroencephalography (EEG) from 5 subjects while driving a car under real conditions along with an accelerometer which detects the onset of steering. Two sensor-level analyses, sample entropy and time-frequency analysis, have been implemented to observe the dynamics before the onset of steering. Thus, in order to classify the steering direction we applied a machine learning algorithm consisting of: dimensionality reduction and classification using principal-component-analysis (PCA) and sup…
An adaptive-PCA algorithm for reflectance estimation from color images
2008
This paper deals with the problem of spectral reflectance estimation from color camera outputs. Because the reconstruction of such functions is an inverse problem, stabilizing the reconstruction process is highly desirable. One way to do this is to decompose reflectance function on a basis functions like PCA. The present work proposes an algorithm making PCA adaptive in reflectance estimation from a color camera output. We propose to adapt the PCA basis derivation by selecting, for each sample, the more relevant elements from the training set elements. The adaptivity criterion is achieved by a likelihood measurement. Finally, the spectral reflectance estimation results are evaluated with th…
Combined spike-related functional MRI and multiple source analysis in the non-invasive spike localization of benign rolandic epilepsy.
2007
Abstract Objective To localize the irritative zone in children by combined spike-related fMRI and EEG multiple source analysis (MSA) in children with benign rolandic epilepsy. Methods Interictal spikes were averaged and localized using MSA, and source locations were displayed in the anatomical 3D-MRI in 11 patients (5–12 yrs, median 10). Interictal spikes were additionally recorded during the fMRI acquisition (EEG-fMRI), and the fMRI sequences were correlated off-line with the EEG spikes. Results MSA revealed an initial central dipole in all patients, including the face or hand area. A second dipolar source was mostly consistent with propagated activity. BOLD activations from EEG-fMRI, cons…
Estimation of total electricity consumption curves of small areas by sampling in a finite population
2016
International audience; Many studies carried out in the French electricity company EDF are based on the analysis of the total electricity consumption curves of groups of customers. These aggregated electricity consumption curves are estimated by using samples of thousands of curves measured at a small time step and collected according to a sampling design. Small area estimation is very usual in survey sampling. It is often addressed by using implicit or explicit domain models between the interest variable and the auxiliary variables. The goal here is to estimate totals of electricity consumption curves over domains or areas. Three approaches are compared: the rst one consists in modeling th…
Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.
2008
Abstract Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components.…