What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
Matt P. Wand mostly deals with Mathematical optimization, Nonparametric regression, Applied mathematics, Statistics and Smoothing.
Matt P. Wand has researched Mathematical optimization in several fields, including Estimator, Density estimation, Polynomial regression, Kernel method and Algorithm.
His studies in Nonparametric regression integrate themes in fields like Semiparametric regression, Total least squares, Generalized linear model and Variance function.
His research on Semiparametric regression concerns the broader Econometrics.
He combines subjects such as Bayesian network, Bayesian inference and Monte Carlo method, Markov chain Monte Carlo with his study of Applied mathematics.
Matt P. Wand focuses mostly in the field of Smoothing, narrowing it down to topics relating to Mixed model and, in certain cases, Additive model, Generalized linear mixed model, Missing data and Restricted maximum likelihood.
His most cited work include:
- Semiparametric Regression: Example Index (1338 citations)
- Multivariate Locally Weighted Least Squares Regression (833 citations)
- An Effective Bandwidth Selector for Local Least Squares Regression (686 citations)
What are the main themes of his work throughout his whole career to date?
Matt P. Wand mainly focuses on Applied mathematics, Statistics, Mathematical optimization, Semiparametric regression and Econometrics.
His Applied mathematics research includes themes of Inference, Estimator, Mixed model, Polynomial and Variational message passing.
Matt P. Wand works mostly in the field of Mixed model, limiting it down to topics relating to Generalized linear mixed model and, in certain cases, Generalized additive model, as a part of the same area of interest.
His research integrates issues of Smoothing, Monte Carlo method, Kernel density estimation, Computation and Multivariate kernel density estimation in his study of Mathematical optimization.
The Semiparametric regression study combines topics in areas such as Semiparametric model and Bayesian probability.
His work carried out in the field of Econometrics brings together such families of science as Parametric statistics, Restricted maximum likelihood and Regression.
He most often published in these fields:
- Applied mathematics (31.44%)
- Statistics (26.20%)
- Mathematical optimization (23.14%)
What were the highlights of his more recent work (between 2017-2021)?
- Inference (14.85%)
- Applied mathematics (31.44%)
- Bayesian inference (10.92%)
In recent papers he was focusing on the following fields of study:
Matt P. Wand mainly investigates Inference, Applied mathematics, Bayesian inference, Random effects model and Factor graph.
Matt P. Wand has included themes like Mean and predicted response, Group, Bayes' theorem and Extension in his Inference study.
His Applied mathematics study frequently links to related topics such as Mixed model.
His research investigates the connection between Bayesian inference and topics such as Theoretical computer science that intersect with issues in Graphical model.
His Kernel method research is within the category of Statistics.
His Statistics study integrates concerns from other disciplines, such as Gradient descent and Feature selection.
Between 2017 and 2021, his most popular works were:
- Semiparametric Regression Analysis via Infer.NET (17 citations)
- Semiparametric Regression with R (8 citations)
- On expectation propagation for generalised, linear and mixed models (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
Inference, Applied mathematics, Factor graph, Variational message passing and Bayes' theorem are his primary areas of study.
His Inference research integrates issues from Markov chain Monte Carlo and Bayesian inference.
The study incorporates disciplines such as Range, Semiparametric regression and Software in addition to Markov chain Monte Carlo.
His research in Bayesian inference intersects with topics in Semiparametric model, Machine learning, Kernel method and Regression.
His studies deal with areas such as Variable and Mixed model as well as Applied mathematics.
His Mean field theory research encompasses a variety of disciplines, including Least squares, Product, Hierarchy, Statistics and Generalized linear mixed model.
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