Not Gaussian Statistics
Mostrando 1-9 de 9 artigos, teses e dissertações.
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1. Effects of exercise on cations/anions in blood serum of English Thoroughbred horses
RESUMO Equinos da raça Puro-Sangue-Inglês são difundidos no México e, devido à falta de dados sobre sua fisiologia do exercício, é importante fazer testes de exercício para obter informações sobre os efeitos do exercício em cátions/ânions mais essenciais no soro do sangue, pois esses equinos são submetidos a esforços constantes. O estudo foi r
Arq. Bras. Med. Vet. Zootec.. Publicado em: 14/06/2019
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2. Conexão entre as redes complexas e estatística de Kaniadakis e busca eficiente das propriedades críticas do processo epidêmico difusivo 1D
Neste trabalho, estudamos a conexão entre uma estatística não Gaussiana, a estatística de Kaniadakis, e as redes complexas. Nós mostramos que a distribuição de conectividades P(k), de uma rede livre de escala, pode ser determinada usando a maximização da entropia de informação no contexto de estatísticas não Gaussianas. Como exemplo, discutimos
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 17/02/2011
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3. Improvement of Wald residual in generalized linear models / Melhoramento do resíduo de Wald em modelos lineares generalizados
The theory of generalized linear models is very used in statistics, not only for modeling data normally distributed, but in the modeling of data whose distribution belongs to the exponential family of distributions. Some examples are binomial, gamma and inverse Gaussian distribution, among others. After tting a model in order to check the adequacy of tting,
Publicado em: 2008
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4. Independent component analysis applied to separation of audio signals. / Análise de componentes independentes aplicada à separação de sinais de áudio.
This work studies Independent Component Analysis (ICA) for instantaneous mixtures, applied to audio signal (source) separation. Three instantaneous mixture separation algorithms are considered: FastICA, PP (Projection Pursuit) and PearsonICA, presenting two common basic principles: sources must be statistically independent and non-Gaussian. In order to analy
Publicado em: 2008
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5. DISTRIBUTIONS OF RETURNS, VOLATILITIES AND CORRELATIONS IN THE BRAZILIAN STOCK MARKET / DISTRIBUIÇÕES DE RETORNOS, VOLATILIDADES E CORRELAÇÕES NO MERCADO ACIONÁRIO BRASILEIRO
The normality assumption is commonly used in the risk management area to describe the distributions of returns standardized by volatilities. However, using five of the most actively traded stocks in Bovespa, this paper shows that this assumption is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when we use the informat
Publicado em: 2004
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6. BAYESIAN LEARNING FOR NEURAL NETWORKS / APRENDIZADO BAYESIANO PARA REDES NEURAIS
This dissertation investigates the Bayesianan Neural Networks, which is a new approach that merges the potencial of the artificial neural networks with the robust analytical analysis of the Bayesian Statistic. Typically, theconventional neural networks such as backpropagation, have good performance but presents problems of convergence, when enough data for t
Publicado em: 1999
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7. Reassessing random-coil statistics in unfolded proteins
The Gaussian-distributed random coil has been the dominant model for denatured proteins since the 1950s, and it has long been interpreted to mean that proteins are featureless, statistical coils in 6 M guanidinium chloride. Here, we demonstrate that random-coil statistics are not a unique signature of featureless polymers. The random-coil model does predict
National Academy of Sciences.
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8. Statistics of the maintained discharge of cat retinal ganglion cells.
Action potentials were recorded from single fibres in the optic tracts of anaesthetized cats. Continuous records were obtained at various levels of scotopic and mesopic retinal illumination. In some cases, the light intensity was modulated by a pseudorandom Gaussian white-noise signal. The maintained discharge of on-centre neurones increased while the mainta
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9. Factor-Analysis Methods for Higher-Performance Neural Prostheses
Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural resp
American Physiological Society.