Gauss Cordeiro | |
|---|---|
 | |
| Born | Gauss Moutinho Cordeiro April 17, 1952 |
| Nationality |  Brazil |
| Alma mater |
|
| Known for |
|
| Scientific career | |
| Doctoral advisor | |
| Website | research |
Gauss Moutinho Cordeiro (born April 17, 1952) is a Brazilian engineer, mathematician and statistician [1] who has made significant contributions to the theory of statistical inference, mainly through asymptotic theory and applied probability.
He received his PhD in Statistics in January 1983 at Imperial College London supervised by David Roxbee Cox and Peter McCullagh. [2]
Currently, Cordeiro is a Class A researcher of the Brazilian Research Council-CNPq, Full Professor at Federal University of Pernambuco (Brazil) and Member of the Graduate Program in Statistics at the same university. He has published more than 360 research articles in international scientific journals with referee practice (2015) and supervised more than 60 MSc dissertations and DSc theses. He was also president of the Associação Brasileira de Estatística, 2000–2002. He was one of the founder editors of the Brazilian Journal of Probability and Statistics and its Editor in Chief between 1995 and 2000. Cordeiro's main research interests in Statistics include Asymptotic Theory, Distribution Theory and Regression Models. He created, developed and organized several statistical meetings in Brazil and abroad. He has acted as referee for several important statistical journals[ citation needed ]. In 2010, Cordeiro awarded from the Brazilian Government the National Medal for Scientific Merit at the order of Comendador. He is a member of the Academy of Sciences of Pernambuco (Brazil).
He married in 1975 and has two daughters. His beloved son Lucas died on June 28, 2022.
The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals made in the results of each individual equation.
In statistics, point estimation involves the use of sample data to calculate a single value which is to serve as a "best guess" or "best estimate" of an unknown population parameter. More formally, it is the application of a point estimator to the data to obtain a point estimate.
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more error-free independent variables. The most common form of regression analysis is linear regression, in which one finds the line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. For specific mathematical reasons, this allows the researcher to estimate the conditional expectation of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters or estimate the conditional expectation across a broader collection of non-linear models.
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model. The various multiple linear regression models may be compactly written as
Sir David Roxbee Cox was a British statistician and educator. His wide-ranging contributions to the field of statistics included introducing logistic regression, the proportional hazards model and the Cox process, a point process named after him.
John Ashworth Nelder was a British statistician known for his contributions to experimental design, analysis of variance, computational statistics, and statistical theory.
Associação Brasileira de Estatística is a non-profit organization with the purpose of promoting the development and application of statistical science. It also acts as a debating place for any professional body that has an interest in statistics.
In econometrics and statistics, a structural break is an unexpected change over time in the parameters of regression models, which can lead to huge forecasting errors and unreliability of the model in general. This issue was popularised by David Hendry, who argued that lack of stability of coefficients frequently caused forecast failure, and therefore we must routinely test for structural stability. Structural stability − i.e., the time-invariance of regression coefficients − is a central issue in all applications of linear regression models.
In statistics, quasi-likelihood methods are used to estimate parameters in a statistical model when exact likelihood methods, for example maximum likelihood estimation, are computationally infeasible. Due to the wrong likelihood being used, quasi-likelihood estimators lose asymptotic efficiency compared to, e.g., maximum likelihood estimators. Under broadly applicable conditions, quasi-likelihood estimators are consistent and asymptotically normal. The asymptotic covariance matrix can be obtained using the so-called sandwich estimator. Examples of quasi-likelihood methods include the generalized estimating equations and pairwise likelihood approaches.
Basílio de Bragança Pereira is a Brazilian statistician.
In statistics, asymptotic theory, or large sample theory, is a framework for assessing properties of estimators and statistical tests. Within this framework, it is often assumed that the sample size n may grow indefinitely; the properties of estimators and tests are then evaluated under the limit of n → ∞. In practice, a limit evaluation is considered to be approximately valid for large finite sample sizes too.
Tristão de Alencar Araripe Júnior was a Brazilian lawyer, literary critic, and writer.
In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homoskedasticity and heteroskedasticity are also frequently used. “Skedasticity” comes from the Ancient Greek word “skedánnymi”, meaning “to scatter”. Assuming a variable is homoscedastic when in reality it is heteroscedastic results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
Álvaro de Barros Lins GCC • GCL was a Brazilian lawyer, journalist, professor and literary critic.
Alberto Guerreiro Ramos was a Brazilian sociologist and politician. An influent Afro-Brazilian thinker, he was instrumental on the development of a native sociological framework, criticizing the use of European paradigms for studying the Brazilian society, especially race relations and the condition of the Black people in Brazil. He advocated for appropriating those concepts to the national reality. He was also a leading figure in organization theory
Carlos Benjamin de Lyra was a prominent Brazilian mathematician, a pioneer in algebraic topology in Brazil and professor at the University of São Paulo.
| International | |
|---|---|
| National | |
| Academics | |
| People | |
