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Hierarchical Dirichlet processes.
 Journal of the American Statistical Association,
, 2006
"... We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this s ..."
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Cited by 942 (78 self)
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. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the wellknown clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we
Neighbourhood components analysis
 Advances in Neural Information Processing Systems 17
, 2004
"... In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leaveoneout KNN score on the training set. It can also learn a lowdimensional linear embedding of labele ..."
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Cited by 346 (9 self)
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of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is nonparametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data
Principal Curves
, 1989
"... Principal curves are smooth onedimensional curves that pass through the middle of a pdimensional data set, providing a nonlinear summary of the data. They are nonparametric, and their shape is suggested by the data. The algorithm for constructing principal curve starts with some prior summary, suc ..."
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Cited by 394 (1 self)
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Principal curves are smooth onedimensional curves that pass through the middle of a pdimensional data set, providing a nonlinear summary of the data. They are nonparametric, and their shape is suggested by the data. The algorithm for constructing principal curve starts with some prior summary
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 185 (19 self)
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is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory
Nonparametric Independent Component Analysis
, 2002
"... We consider the problem of nonparametric estimation of ddimensional probability density and its "principal directions" in the model of Independent Component Analysis. A new method of estimation based on diagonalization of nonparametric estimates of certain matrix functionals of the den ..."
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Cited by 12 (1 self)
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We consider the problem of nonparametric estimation of ddimensional probability density and its "principal directions" in the model of Independent Component Analysis. A new method of estimation based on diagonalization of nonparametric estimates of certain matrix functionals
Component selection and smoothing in multivariate nonparametric regression
"... We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The “COSSO ” is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in ..."
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Cited by 76 (1 self)
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We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The “COSSO ” is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed
Nonparametric dependent components
 In Proceedings of ICASSP’05, IEEE International Conference on Acoustics, Speech, and Signal Processing
, 2005
"... Reprinted with permission. ..."
Nonparametric Independent Component Analysis
, 2002
"... Abstract We consider the problem of nonparametric estimation of ddimensional probability density and its "principal directions " in the model of Independent Component Analysis. A new method of estimation based on diagonalization of nonparametric estimates of certain matrix functio ..."
Abstract
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Abstract We consider the problem of nonparametric estimation of ddimensional probability density and its "principal directions " in the model of Independent Component Analysis. A new method of estimation based on diagonalization of nonparametric estimates of certain matrix
VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS
, 2008
"... Summary. We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is “small” relative to the sample size. The statistical problem is to determin ..."
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Cited by 65 (1 self)
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Summary. We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is “small” relative to the sample size. The statistical problem
Results 1  10
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