What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
Michael L. Stein mainly investigates Covariance function, Covariance, Random field, Mathematical analysis and Kriging.
His Covariance function research is multidisciplinary, relying on both Fisher information, Computation and Applied mathematics.
His Fisher information research is multidisciplinary, incorporating perspectives in Asymptotically optimal algorithm, Autocovariance, Multivariate normal distribution and Autocorrelation.
His work deals with themes such as Mathematical optimization and Econometrics, which intersect with Covariance.
His work carried out in the field of Random field brings together such families of science as Fractional Brownian motion, Brownian motion and Estimator.
His work on Variance reduction, Delta method, Sampling and Latin hypercube sampling as part of his general Statistics study is frequently connected to Simple random sample, thereby bridging the divide between different branches of science.
His most cited work include:
- Interpolation of Spatial Data: Some Theory for Kriging (1997 citations)
- Interpolation of Spatial Data (1351 citations)
- Large sample properties of simulations using latin hypercube sampling (1156 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Applied mathematics, Covariance, Covariance function, Statistics and Random field.
His studies in Applied mathematics integrate themes in fields like Differentiable function, Covariance matrix, Mathematical optimization and Estimator.
His Covariance research includes elements of Estimation theory, Fisher information and Statistical model.
His research integrates issues of Estimation of covariance matrices and Kriging in his study of Covariance function.
Michael L. Stein combines topics linked to Econometrics with his work on Statistics.
His Random field research integrates issues from Statistical physics, Mathematical analysis and Spectral density.
He most often published in these fields:
- Applied mathematics (24.32%)
- Covariance (21.08%)
- Covariance function (21.08%)
What were the highlights of his more recent work (between 2016-2021)?
- Gaussian process (18.38%)
- Applied mathematics (24.32%)
- Covariance (21.08%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Gaussian process, Applied mathematics, Covariance, Remote sensing and Quantile regression.
His studies deal with areas such as Parametric model, Climate model, Generalized Pareto distribution, Estimator and Covariance function as well as Applied mathematics.
He has included themes like Separable space, Representation, Spectral density and Class in his Covariance function study.
Michael L. Stein has researched Covariance in several fields, including Algorithm and Kriging.
His Algorithm research includes themes of Expectation–maximization algorithm, Spatial analysis and Bayesian statistics, Bayesian inference.
His studies in Kriging integrate themes in fields like Sampling, Uncertainty quantification, Stochastic modelling and Random field.
Between 2016 and 2021, his most popular works were:
- Bayesian and Maximum Likelihood Estimation for Gaussian Processes on an Incomplete Lattice (35 citations)
- Locally stationary spatio-temporal interpolation of Argo profiling float data. (30 citations)
- An Inversion-Free Estimating Equations Approach for Gaussian Process Models (14 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
Michael L. Stein spends much of his time researching Gaussian process, Applied mathematics, Covariance function, Algorithm and Covariance matrix.
The concepts of his Applied mathematics study are interwoven with issues in Class, Covariance, Representation and Spectral density.
Covariance function is often connected to Separable space in his work.
His study in the field of Computation is also linked to topics like Scalability.
His work carried out in the field of Covariance matrix brings together such families of science as Scale parameter, Estimator, Mathematical optimization and Exponential function.
His Mathematical optimization research is multidisciplinary, incorporating elements of CMA-ES, Estimation of covariance matrices, Matrix-free methods and Cholesky decomposition.
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