Sobre
Nascido a 15/09/71 possui licenciatura e mestrado em Matemática Aplicada e é Doutor em Ciências de Engenharia. Desenvolve trabalho de investigação em estatística e análise de dados e na transformação Box-Cox.
Nascido a 15/09/71 possui licenciatura e mestrado em Matemática Aplicada e é Doutor em Ciências de Engenharia. Desenvolve trabalho de investigação em estatística e análise de dados e na transformação Box-Cox.
Nascido a 15/09/71 possui licenciatura e mestrado em Matemática Aplicada e é Doutor em Ciências de Engenharia. Desenvolve trabalho de investigação em estatística e análise de dados e na transformação Box-Cox.
2022
Autores
Goncalves, R;
Publicação
INNOVATIONS IN INDUSTRIAL ENGINEERING
Abstract
In an earlier work we described and applied a methodology to find an adequate distribution for Nearly Gaussian (NG) random variables. In this work, we compare two different methods, m1 and m2 to estimate a power transform parameter for NG random variables. The m1 method is heuristic and based on sample kurtosis. Herein, we describe and apply it using a new reduced data set. The second method m2 is based on the maximization of a pseudo-log-likelihood function. As an application, we compare the performance of each method using high power statistical tests for the null hypothesis of normality. The data we use are the daily errors in the forecasts of maximum and minimum temperatures in the city of Porto. We show that the high kurtosis of the original data is due to high correlation among data. We also found that although consistent with normality the data is better fitted by distributions of the power normal (PN) family than by the normal distribution. Regarding the comparison of the two parameter estimation methods we found that the m1 provides higher p-values for the observed statistics tests except for the Shapiro-Wilk test.
2019
Autores
Goncalves, R;
Publicação
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM-2018)
Abstract
The Power Normal (PN) family of distributions is obtained by inverting the Box-Cox (BC) transformation over a truncated normal (TN) (or for some cases normal) random variable. In this paper we explore the PN distribution. We give a formula for the ordinary moments and considering the bivariate PN (BPN) distribution we calculate the marginal and conditional probability density functions (pdf). We prove that they are not univariate PN distributed. We also calculate the correlation curve and we fit a power law model.
2014
Autores
Ferreira, H; Goncalves, R; Pinto, AA;
Publicação
Springer Proceedings in Mathematics and Statistics
Abstract
2012
Autores
Xavier Romão; Rui Gonçalves; Aníbal Costa
Publicação
Mat. and Struct. - Materials and Structures
Abstract
A probabilistic framework is defined to
evaluate the values of the Confidence Factors (CFs)
proposed in Eurocode 8 Part 3 (EC8-3) for the
characterization of material properties. This evaluation
is presented for the concrete compressive strength
but its validity for other material properties can also be
inferred from the results obtained. The number of
material tests and the existence of prior knowledge are
the essential aspects for the CF quantification. The
probabilistic framework proposed in the first part of
the study does not consider the existence of prior
knowledge and is based on the concept of confidence
intervals. In the second part of the study, the effects of
prior knowledge are considered using a Bayesian
framework. The combination of testing data obtained
from different types of tests is also addressed as an
extension of the referred Bayesian approach. Results
indicate that the EC8-3 proposed CFs for KL1 and
KL2 are adequate, but for KL3 it is suggested that a
larger valu
2011
Autores
Rui Gonçalves; Helena Ferreira; Alberto Pinto
Publicação
Journal of Difference Equations and Applications - Journal of Difference Equations and Applications, vol.17, no.7, pp.1049-1063
Abstract
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