Search results for "Statistic"
showing 10 items of 12520 documents
Alteraciones osteoarticulares en el músico adulto mayor de instrumentos viento-madera
2021
espanolRESUMEN Objetivo: Valorar si existen cambios a nivel bucodental en adultos mayores, instrumentistas de viento-madera por el uso del instrumento a lo largo de mas de 35 anos de profesion y compararlos con un grupo control. Metodologia: Estudio de tipo observacional, descriptivo de corte transversal, entre musicos profesionales, adultos mayores, de 60 anos o mas, con mas de 35 anos de profesion y que toquen instrumentos de "viento-madera". La muestra inicial de estudio estaba formada por 57 musicos. El instrumento de recogida de datos utilizado se ha elaborado de forma exclusiva. El analisis estadistico se llevo a cabo mediante SPSS Statistics 23.0. Resultados: Media de edad 63 ± 1,28 …
Do Firms Share the Same Functional Form of Their Growth Rate Distribution? A New Statistical Test
2011
We propose a hypothesis testing procedure to investigate whether the same growth rate distribution is shared by all the firms in a balanced panel or, more generally, whether they share the same functional form for this distribution, without necessarily sharing the same parameters. We apply the test to panels of US and European Union publicly quoted manufacturing firms, both at the sectoral and at the subsectoral NAICS levels. We consider the following null hypotheses about the growth rate distribution of the individual firms: i) an unknown shape common to all firms, with all the firms sharing also the same parameters, or with the firm variance related to its firm size through a scaling rela…
Machine Learning Methods for Spatial and Temporal Parameter Estimation
2020
Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…
The University of Valencia’s computerized word pool
1988
This paper presents the University of Valencia’s computerized word pool. This is a database that includes 16,109 Spanish words, together with 11 psychological variables for limited groups of items. The purpose behind the creation of this database was to have available a large quantity of verbal stimuli in a well-controlled system, ready for automatic selection. The description includes a summary of statistics on each of the 11 psychological variables, together with a correlational and factor analysis of them. This statistical analysis produces results close to those obtained for equivalent English material.
Semisupervised kernel orthonormalized partial least squares
2012
This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…
Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis
2014
This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…
Evidentials and Epistemic Modality
2018
Abstract This chapter deals with the relation between the notional domains of information source and epistemic modality. It surveys various approaches to this relation and the cross-linguistic patterns of the way in which linguistic units (of diverse formats) with evidential or epistemic meanings develop extensions whereby they encroach into each other’s domains. Meaning extensions in either direction can adequately be captured, and confusion between both domains can be avoided, only if in the analysis of the meaning of such units (a) an onomasiological and semasiological perspective and (b) a coded-inferred divide are distinguished. Thus, epistemic extensions often arise as Generalized Con…
Latent Semantic Description of Iconic Scenes
2005
It is proposed an approach for the automatic description of scenes using a LSA–like technique. The described scenes are composed by a set of elements that can be geometric forms or iconic representation of objects. Every icon is characterized by a set of attributes like shape, colour and position. Each scene is related to a set of sentences describing their content. The proposed approach builds a data driven vector semantic space where the scenes and the sentences are mapped. A new scene can be mapped in this created space accordingly to a suitable metric. Preliminary experimental results show the effectiveness of the procedure.
A word prediction methodology for automatic sentence completion
2015
Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network lang…
A family of kernel anomaly change detectors
2014
This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces with kernel dependence estimates, provide low-rank kernel versions to cope with the high computational cost of the methods, and give prescriptions about the selection of the kernel functions and their parameters. We illustrate the performance of the introduced kernel methods in both pervasive and anom…