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CSDA
2007
108views more  CSDA 2007»
14 years 11 months ago
Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) phenotypes. An EM algorithm for maxim...
Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio
BMCBI
2004
90views more  BMCBI 2004»
14 years 11 months ago
Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays
Background: Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normal...
Igor Dozmorov, Nicholas Knowlton, Yuhong Tang, Mic...
CVPR
2007
IEEE
16 years 1 months ago
Robust Estimation of Texture Flow via Dense Feature Sampling
Texture flow estimation is a valuable step in a variety of vision related tasks, including texture analysis, image segmentation, shape-from-texture and texture remapping. This pap...
Yu-Wing Tai, Michael S. Brown, Chi-Keung Tang
NPL
2000
95views more  NPL 2000»
14 years 11 months ago
Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). Although a deterministic search algo...
Akio Utsugi
CORR
2010
Springer
127views Education» more  CORR 2010»
14 years 11 months ago
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stoch...
Joshua Dillon, Guy Lebanon