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Iain M. Johnstone
Researcher at Stanford University
Publications - 113
Citations - 31982
Iain M. Johnstone is an academic researcher from Stanford University. The author has contributed to research in topics: Minimax & Estimator. The author has an hindex of 54, co-authored 111 publications receiving 29434 citations. Previous affiliations of Iain M. Johnstone include University of Oxford & Australian National University.
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Speed of Estimation in Positron Emission Tomography and Related Inverse Problems
TL;DR: In this article, the authors considered a continuous idealization of the PET reconstruction problem, considered as an example of bivariate density estimation based on indirect observations, and established exact minimax rates of convergence of estimation, for all possible estimators, over suitable smoothness classes of functions.
Posted Content
Sparse Principal Components Analysis
Iain M. Johnstone,Arthur Yu Lu +1 more
TL;DR: In this article, a simple "sparse PCA" algorithm was proposed to estimate eigenvectors from PCA on the selected subset, threshold and reexpress in the original basis.
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Projection-Based Approximation and a Duality with Kernel Methods
TL;DR: Projection pursuit regression and kernel regression are methods for estimating a smooth function of several variables from noisy data obtained at scattered sites as discussed by the authors, and they are complementary: for a given function, if one method offers a dimensionality reduction, the other does not.
Wavelet shrinkage for correlated data and inverse problems: adaptivity results
TL;DR: In this paper, it was shown that level-dependent thresholding methods are simultaneously asymptotically minimax up to constants over a broad range of linear inverse problems possessing a wavelet vaguelette decomposition.
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Prediction of prostate cancer volume using prostate-specific antigen levels, transrectal ultrasound, and systematic sextant biopsies
TL;DR: The formula for prediction of cancer volume correlates well with actual cancer volume in 92 patients but is not adequate to predict volume for an individual patient, and the formulas for predictions of volume range show promising predictive ability and may be useful if the extent of disease is unclear.