168 citations · 460 across the 3 of their papers we have counts for
8 papers
Sparse Principal Components Analysis
Iain M Johnstone, Arthur Yu Lu
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. For a s…
Multivariate analysis and Jacobi ensembles: largest eigenvalue, Tracy--Widom limits and rates of convergence
Iain M. Johnstone
Let and be independent, central Wishart matrices in variables with common covariance and having and degrees of freedom, respectively. The distribution of the la…
High Dimensional Statistical Inference and Random Matrices
Iain M. Johnstone
Multivariate statistical analysis is concerned with observations on several variables which are thought to possess some degree of inter-dependence. Driven by problems in genetics a…
Empirical Bayes selection of wavelet thresholds
Iain M. Johnstone, Bernard W. Silverman
This paper explores a class of empirical Bayes methods for level-dependent threshold selection in wavelet shrinkage. The prior considered for each wavelet coefficient is a mixture…
Adapting to Unknown Sparsity by controlling the False Discovery Rate
Felix Abramovich, Yoav Benjamini, David L. Donoho +1
We attempt to recover an -dimensional vector observed in white noise, where is large and the vector is known to be sparse, but the degree of sparsity is unknown. We consider…
Periodic boxcar deconvolution and diophantine approximation
Iain M. Johnstone, Marc Raimondo
We consider the nonparametric estimation of a periodic function that is observed in additive Gaussian white noise after convolution with a ``boxcar,'' the indicator function of an…