What is he best known for?
The fields of study he is best known for:
- Statistics
- Botany
- Normal distribution
The scientist’s investigation covers issues in Applied mathematics, Statistics, Weibull distribution, Exponentiated Weibull distribution and Order statistic.
His research in Applied mathematics intersects with topics in Generating function, Quantile, Mathematical optimization, Skewness and Kurtosis.
Statistics connects with themes related to Econometrics in his study.
His Weibull distribution study combines topics from a wide range of disciplines, such as Failure rate, Exponential distribution, Moment-generating function and Extreme value theory.
In his study, which falls under the umbrella issue of Exponentiated Weibull distribution, Generalized integer gamma distribution and Natural exponential family is strongly linked to Generalized beta distribution.
Burr distribution is closely connected to Beta distribution in his research, which is encompassed under the umbrella topic of Order statistic.
His most cited work include:
- The Kumaraswamy Weibull distribution with application to failure data (205 citations)
- A generalized modified Weibull distribution for lifetime modeling (193 citations)
- Generalized beta-generated distributions (188 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Statistics, Applied mathematics, Regression analysis, Weibull distribution and Exponentiated Weibull distribution.
He has researched Statistics in several fields, including Residual and Econometrics.
His research in Applied mathematics is mostly concerned with Order statistic.
His Regression analysis research incorporates elements of Logarithm and Estimator, Censoring.
His research integrates issues of Failure rate, Exponential distribution, Quantile function, Moment-generating function and Extreme value theory in his study of Weibull distribution.
Much of his study explores Exponentiated Weibull distribution relationship to Weibull modulus.
He most often published in these fields:
- Statistics (53.56%)
- Applied mathematics (39.75%)
- Regression analysis (34.73%)
What were the highlights of his more recent work (between 2017-2021)?
- Regression analysis (34.73%)
- Statistics (53.56%)
- Applied mathematics (39.75%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Regression analysis, Statistics, Applied mathematics, Residual and Maximum likelihood.
The various areas that Edwin M. M. Ortega examines in his Regression analysis study include Econometrics, Weibull distribution, Distribution, Monte Carlo method and Censoring.
His work in the fields of Weibull distribution, such as Exponentiated Weibull distribution, intersects with other areas such as Linear representation and Fraction.
His study deals with a combination of Statistics and Fraction.
His biological study focuses on Order statistic.
His Maximum likelihood study which covers Normal distribution that intersects with Divergence.
Between 2017 and 2021, his most popular works were:
- The Kumaraswamy Generalized Half-Normal Distribution for Skewed Positive Data (42 citations)
- The Burr XII System of densities: properties, regression model and applications (34 citations)
- The odd Lomax generator of distributions: Properties, estimation and applications (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Botany
- Normal distribution
Edwin M. M. Ortega focuses on Applied mathematics, Regression analysis, Statistics, Order statistic and Quantile.
His Applied mathematics research is multidisciplinary, incorporating elements of Parametric statistics, Generating function, Maximum likelihood, Weibull distribution and Residual.
His work in Weibull distribution addresses issues such as Distribution, which are connected to fields such as Burr distribution.
He usually deals with Regression analysis and limits it to topics linked to Sample size determination and Censoring.
His work on Regression, Generalized integer gamma distribution and Random variable as part of general Statistics study is frequently linked to Oil production, bridging the gap between disciplines.
His research in Quantile intersects with topics in Probability density function and Gamma distribution.
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