What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Regression analysis
His primary areas of study are Regression analysis, Statistics, Generalized linear model, Econometrics and Covariate.
His studies in Regression analysis integrate themes in fields like Ordinal data, Estimator, Categorical variable and Regression.
His Statistics study integrates concerns from other disciplines, such as Boosting and Feature selection.
Many of his studies on Econometrics involve topics that are commonly interrelated, such as Variables.
His Covariate research includes elements of Nonparametric statistics, Data mining, Hierarchical generalized linear model, Generalized linear mixed model and Mathematical optimization.
The Nonparametric regression study combines topics in areas such as Computational statistics, Random forest and Recursive partitioning.
His most cited work include:
- An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests (1359 citations)
- Multivariate Statistical Modelling Based on Generalized Linear Models (1237 citations)
- Regression for Categorical Data (164 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Econometrics, Statistics, Covariate, Feature selection and Artificial intelligence.
His study in Econometrics is interdisciplinary in nature, drawing from both Logistic regression, Generalized linear model, Multinomial logistic regression and Linear model.
His Covariate research includes themes of Data mining, Regularization, Parametric statistics and Nonparametric statistics.
He has researched Feature selection in several fields, including Estimator, Lasso and Selection.
His research integrates issues of Algorithm, Machine learning and Pattern recognition in his study of Artificial intelligence.
His Smoothing research incorporates themes from Kernel, Mathematical optimization, Nonparametric regression and Applied mathematics.
He most often published in these fields:
- Econometrics (36.65%)
- Statistics (33.86%)
- Covariate (21.12%)
What were the highlights of his more recent work (between 2017-2021)?
- Econometrics (36.65%)
- Statistics (33.86%)
- Covariate (21.12%)
In recent papers he was focusing on the following fields of study:
Gerhard Tutz mainly focuses on Econometrics, Statistics, Covariate, Recursive partitioning and Ordinal regression.
His Econometrics study combines topics in areas such as Logistic regression and Multinomial logistic regression.
His work in the fields of Ordered logit, Additive model and Range overlaps with other areas such as Random effects model.
His Covariate study incorporates themes from Regression analysis, Strongly connected component, Parametric statistics and Selection.
As a part of the same scientific family, Gerhard Tutz mostly works in the field of Tree, focusing on Regression and, on occasion, Machine learning.
His Artificial intelligence research incorporates elements of Generalized linear model, Linear model and Categorical variable.
Between 2017 and 2021, his most popular works were:
- Modeling Discrete Time-to-Event Data (31 citations)
- Modelling uncertainty and response styles in ordinal data (15 citations)
- Response Styles in the Partial Credit Model (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
His scientific interests lie mostly in Econometrics, Statistics, Mixture model, Component and Ordinal data.
His study in the field of Logit also crosses realms of Random effects model.
His work on Ordered logit is typically connected to German, Sequential model and Response analysis as part of general Statistics study, connecting several disciplines of science.
His biological study spans a wide range of topics, including Uniform distribution and Applied mathematics.
His Contrast research is multidisciplinary, incorporating perspectives in Generalized linear model and Artificial intelligence.
His Recursive partitioning study combines topics from a wide range of disciplines, such as Regularization, Covariate, Data mining and Regression.
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