Perspectives on big data analysis : methodologies and applications : International Workshop on Perspectives on High-Dimension Data Anlaysis II, May 30-June 1, 2012, Centre de recherches mathématiques, University de Montréal, Montréal, Québec, Canada /

Gespeichert in:
Weitere Titel:
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 /
1. Verfasser:
Ahmed, S. E.
Körperschaft:
International Workshop on Perspectives on High-Dimension Data Anlaysis Montréal, Québec
Format:
Elektronisch Tagungsbericht E-Book
Sprache:
Englisch
Veröffentlicht:
Providence, Rhode Island : American Mathematical Society, 2014.
Umfang:
1 online resource (pages cm.)
Mode of access : World Wide Web
Format Details:
Mode of access : World Wide Web
Bibliografie:
Includes bibliographical references and index.
Schriftenreihe:
Contemporary mathematics,
Sekundärform:
Publikations­art: Electronic reproduction.
Verlag: Providence, Rhode Island :: American Mathematical Society.
Publikations­datum: 2014
ISBN:
9781470418878 (online)
ISSN:
1098-3627 ;
0271-4132
DOI:
10.1090/conm/622
Zugangseinschränkungen:
Access is restricted to licensed institutions
Schlagworte:
Multivariate analysis > Congresses.
Artificial intelligence > Congresses.
Big data > Congresses.
Computer science > Artificial intelligence > None of the above, but in this section.
Statistics > Multivariate analysis > Factor analysis and principal components; correspondence analysis.
Statistics > Linear inference, regression > Ridge regression; shrinkage estimators.
Statistics > Parametric inference > Asymptotic properties of tests.
Statistics > Nonparametric inference > Estimation.
Statistics > Inference from stochastic processes > Markov processes: estimation.
Statistics > Nonparametric inference > Nonparametric regression.
Probability theory and stochastic processes.
Statistics > Nonparametric inference > None of the above, but in this section.
Statistics > Multivariate analysis > Hypothesis testing.
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