UID:
almahu_9947417292702882
Format:
1 online resource (xi, 191 pages)
Edition:
Electronic reproduction. Providence, Rhode Island : American Mathematical Society. 2014
ISBN:
9781470418878 (online)
Series Statement:
Contemporary mathematics, v. 622
Note:
Principal Component Analysis (PCA) for high-dimensional data. PCA is dead. Long live PCA /
,
Solving a System of High-Dimensional Equations by MCMC /
,
A slice sampler for the hierarchical Poisson/Gamma random field model /
,
A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model /
,
Efficient adaptive estimation strategies in high-dimensional partially linear regression models /
,
Geometry and properties of generalized ridge regression in high dimensions /
,
Multiple testing for high-dimensional data /
,
On multiple contrast tests and simultaneous confidence intervals in high-dimensional repeated measures designs /
,
Data-driven smoothing can preserve good asymptotic properties /
,
Variable selection for ultra-high-dimensional logistic models /
,
Shrinkage estimation and selection for a logistic regression model /
,
Manifold unfolding by Isometric Patch Alignment with an application in protein structure determination /
,
Mode of access : World Wide Web
Additional Edition:
Print version: Perspectives on big data analysis : ISSN 0271-4132 ISBN 9781470410421
Language:
English