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A Corrected Score Approach for Proportional Hazards Model With Error-Contaminated Covariates Subject to Detection Limits. [PDF]
Stat MedSong X, Wang CY.
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2010
AbstractA difficulty of nonparametric techniques is the curse of dimensionality. One cannot estimate nonparametrically in high dimensions unless restrictions are made. The present chapter considers a selection of such more restrictive models. The techniques are intermediate between purely parametric and purely nonparametric.
Timo Teräsvirta +2 more
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AbstractA difficulty of nonparametric techniques is the curse of dimensionality. One cannot estimate nonparametrically in high dimensions unless restrictions are made. The present chapter considers a selection of such more restrictive models. The techniques are intermediate between purely parametric and purely nonparametric.
Timo Teräsvirta +2 more
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Bayesian nonparametric hierarchical modeling
Biometrical Journal, 2009AbstractIn biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects ...
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Proceedings of the 12th annual ACM international conference on Multimedia, 2004
Motion information is a powerful cue for visual perception. In the context of video indexing and retrieval, motion content serves as a useful source for compact video representation. There has been a lot of literature about parametric motion models. However, it is hard to secure a proper parametric assumption in a wide range of video scenarios. Diverse
Ling-Yu Duan +3 more
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Motion information is a powerful cue for visual perception. In the context of video indexing and retrieval, motion content serves as a useful source for compact video representation. There has been a lot of literature about parametric motion models. However, it is hard to secure a proper parametric assumption in a wide range of video scenarios. Diverse
Ling-Yu Duan +3 more
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Nonparametric Graphical Models
2018This chapter introduces different approaches to graphical modeling for continuous and mixed data, using semiparametric techniques that make weak assumptions compared with the default Gaussian graphical model. It outlines some different ways of making restrictions on the model that lead to computationally tractable models with favorable statistical ...
Han Liu, John Lafferty
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A New Nonparametric Growth Model
IEEE Transactions on Reliability, 1987This paper proposes a new nonparametric reliability growth model for the analysis of the failure rate of a system that is undergoing development test. The only restrictions on the actual, unknown failure distribution for each stage of testing is that it be continuous, have only one unknown parameter \(\theta\), and have an associated unimodal ...
Robinson, David G., Dietrich, Duane
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2015
We briefly review some of the nonparametric Bayesians models that are most widely used in biostatistics and bioinformatics. We define the Dirichlet process, Dirichlet process mixtures, the Polya tree, the dependent Dirichlet process and the Gaussian process prior.
Peter Müller, Riten Mitra
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We briefly review some of the nonparametric Bayesians models that are most widely used in biostatistics and bioinformatics. We define the Dirichlet process, Dirichlet process mixtures, the Polya tree, the dependent Dirichlet process and the Gaussian process prior.
Peter Müller, Riten Mitra
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Nonparametric Time Series Modeling
2004Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume.
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Non-parametric Structural Models
2008The interplay between economic theory and econometrics comes to its full force when analysing structural models. These models are used in industrial organization, marketing, public finance, labour economics and many other fields in economics. Structural econometric methods make use of the behavioural and equilibrium assumptions specified in economic ...
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