What is he best known for?
The fields of study he is best known for:
- Statistics
- Algebra
- Geometry
His main research concerns Statistics, Regression analysis, Econometrics, Applied mathematics and Combinatorics.
Statistics and Mathematical optimization are frequently intertwined in his study.
In his study, Matrix representation and Mathematical proof is strongly linked to Linear regression, which falls under the umbrella field of Regression analysis.
His work carried out in the field of Econometrics brings together such families of science as Estimation and Regression.
His Response surface methodology research incorporates themes from Transformation, Ridge, Lack-of-fit sum of squares and Least squares.
His studies in Segmented regression integrate themes in fields like Regression dilution, Generalized linear model, Local regression and Factor regression model.
His most cited work include:
- Applied Regression Analysis (18298 citations)
- Empirical Model-Building and Response Surfaces (4589 citations)
- Applied regression analysis 2nd ed. (3054 citations)
What are the main themes of his work throughout his whole career to date?
Norman R. Draper mainly investigates Statistics, Applied mathematics, Econometrics, Regression analysis and Combinatorics.
Norman R. Draper regularly links together related areas like Surface in his Statistics studies.
His research in Applied mathematics intersects with topics in Mixture model and Mathematical optimization.
His Econometrics study incorporates themes from Outlier and Bayesian multivariate linear regression.
His work deals with themes such as Linear regression and Bibliography, which intersect with Regression analysis.
Norman R. Draper is conducting research in Total least squares and Least squares as part of his Lack-of-fit sum of squares study.
He most often published in these fields:
- Statistics (39.45%)
- Applied mathematics (19.53%)
- Econometrics (16.02%)
What were the highlights of his more recent work (between 2007-2014)?
- Statistics (39.45%)
- Mathematical optimization (10.94%)
- Applied mathematics (19.53%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Statistics, Mathematical optimization, Applied mathematics, Econometrics and Art history.
His study involves Regression analysis, Regression, Autocorrelation, Durbin–Watson statistic and Total sum of squares, a branch of Statistics.
While the research belongs to areas of Mathematical optimization, Norman R. Draper spends his time largely on the problem of Factorial experiment, intersecting his research to questions surrounding Design of experiments and Plackett–Burman design.
His Applied mathematics study combines topics from a wide range of disciplines, such as Explained sum of squares, Quadratic equation, Linear model, Transformation and Least squares.
His study in Least squares is interdisciplinary in nature, drawing from both Coefficient of determination and Mathematical analysis.
The Econometrics study combines topics in areas such as Polynomial regression and Local regression.
Between 2007 and 2014, his most popular works were:
- Fitting a Straight Line by Least Squares (35 citations)
- Selecting the “Best” Regression Equation (22 citations)
- An Introduction to Nonlinear Estimation (12 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algebra
- Geometry
Norman R. Draper mainly focuses on Statistics, Mathematical optimization, Applied mathematics, Least squares and Econometrics.
A large part of his Statistics studies is devoted to Regression analysis.
As a part of the same scientific family, he mostly works in the field of Mathematical optimization, focusing on Factorial experiment and, on occasion, Design of experiments, Engineering drawing and Equidistant.
The concepts of his Applied mathematics study are interwoven with issues in Quadratic equation, D optimality and Combinatorics, Composition.
His study explores the link between Least squares and topics such as Explained sum of squares that cross with problems in Residual sum of squares and Method of steepest descent.
His research on Econometrics also deals with topics like
- Polynomial regression and related Analysis of covariance, Variance function, Unit-weighted regression and Variance-based sensitivity analysis,
- Local regression and related One-way analysis of variance, Simple linear regression and Bayesian multivariate linear regression.
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