What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
His primary scientific interests are in Wavelet, Mathematical optimization, Minimax, Algorithm and Density estimation.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Estimator, Nonparametric regression, Wavelet noise, Applied mathematics and Wavelet transform.
His research in Estimator intersects with topics in Polynomial and Gaussian noise.
He works mostly in the field of Wavelet noise, limiting it down to topics relating to Piecewise and, in certain cases, Decision theory.
Iain M. Johnstone combines subjects such as Statistics and Feature selection with his study of Algorithm.
His work on Marginal likelihood, Least-angle regression, Elastic net regularization and Regression analysis as part of general Statistics study is frequently linked to Posterior probability, bridging the gap between disciplines.
His most cited work include:
- Least angle regression (6995 citations)
- Ideal spatial adaptation by wavelet shrinkage (6645 citations)
- Adapting to Unknown Smoothness via Wavelet Shrinkage (3774 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Applied mathematics, Minimax, Mathematical optimization, Wavelet and Statistics.
His research in Applied mathematics tackles topics such as Eigenvalues and eigenvectors which are related to areas like Principal component analysis, Combinatorics and Sample.
His research integrates issues of Discrete mathematics, Norm, Density estimation and Compressed sensing in his study of Minimax.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Function, Estimator and Smoothness.
His studies deal with areas such as Algorithm and Bounded variation as well as Wavelet.
His work deals with themes such as Linear regression, Ordinary least squares, Least-angle regression, Wavelet noise and Fourier transform, which intersect with Algorithm.
He most often published in these fields:
- Applied mathematics (33.33%)
- Minimax (23.68%)
- Mathematical optimization (20.18%)
What were the highlights of his more recent work (between 2010-2021)?
- Applied mathematics (33.33%)
- Eigenvalues and eigenvectors (9.65%)
- Minimax (23.68%)
In recent papers he was focusing on the following fields of study:
Iain M. Johnstone mainly investigates Applied mathematics, Eigenvalues and eigenvectors, Minimax, Covariance matrix and Mathematical optimization.
The concepts of his Applied mathematics study are interwoven with issues in Mean squared error, Estimator and Covariance.
In Mean squared error, Iain M. Johnstone works on issues like Function, which are connected to Range.
His study looks at the relationship between Eigenvalues and eigenvectors and fields such as Sample, as well as how they intersect with chemical problems.
His Minimax study incorporates themes from Density estimation and Compressed sensing.
His Covariance matrix study also includes fields such as
- Principal component analysis which is related to area like Outlier, Linear model and Aliasing,
- Expected value which intersects with area such as Tracy–Widom distribution, High dimensional and Edge.
Between 2010 and 2021, his most popular works were:
- Normalization, testing, and false discovery rate estimation for RNA-sequencing data (248 citations)
- Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising (168 citations)
- Innate immune response to homologous rotavirus infection in the small intestinal villous epithelium at single-cell resolution (57 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
Iain M. Johnstone mostly deals with Minimax, Applied mathematics, Compressed sensing, Eigenvalues and eigenvectors and Covariance matrix.
His Minimax study is concerned with Mathematical optimization in general.
His Estimator research extends to Mathematical optimization, which is thematically connected.
His Compressed sensing study combines topics from a wide range of disciplines, such as Mean squared error, Matrix and Undersampling.
His biological study deals with issues like Lasso, which deal with fields such as Combinatorics.
In his research on the topic of Eigenvalues and eigenvectors, Sample, Sample size determination and Statistical hypothesis testing is strongly related with Principal component analysis.
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