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- Yuhong Yang, Andrew Barron
- 1995

In this paper, we present some general results determining minimax bounds on statistical risk for density estimation based on certain information-theoretic considerations. These bounds depend only on metric entropy conditions and are used to identify the minimax rates of convergence.

- Yuhong Yang, Andrew R. Barron
- IEEE Trans. Information Theory
- 1998

Probability models are estimated by use of penalized log-likelihood criteria related to AIC and MDL. The accuracies of the density estimators are shown to be related to the tradeoff between three terms: the accuracy of approximation, the model dimension, and the descriptive complexity of the model classes. The asymptotic risk is determined under conditions… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 1999

This paper studies minimax aspects of nonparametric classification. We first study minimax estimation of the conditional probability of a class label, given the feature variable. This function, say f , is assumed to be in a general nonparametric class. We show the minimax rate of convergence under square L2 loss is determined by the massiveness of the class… (More)

- Yuhong Yang
- 1999

Methods have been proposed to linearly combine candidate regression procedures to improve estimation accuraccy. Applications of these methods in many examples are very succeesful, pointing to the great potential of combining procedures. A fundamental question regarding combining procedure is: What is the potential gain and how much one needs to pay for it?… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 1999

|We study nonparametric estimation of a conditional probability for classiication based on a collection of nite-dimensional models. For the sake of exibility, diierent types of models, linear or nonlinear, are allowed as long as each satisses a dimensionality assumption. We show that with a suitable model selection criterion, the penalized maximum… (More)

- Yuhong Yang
- AISTATS
- 2007

Efforts have been directed at obtaining flexible learning procedures that optimally adapt to various possible characteristics of the data generating mechanism. A question that addresses the issue of how far one can go in this direction is: Given a regression procedure, however sophisticated it is, how many regression functions are estimated accurately? In… (More)

- Wei Qian, Yuhong Yang
- 2012

The adaptive lasso is a model selection method shown to be both consistent in variable selection and asymptotically normal in coefficient estimation. The actual variable selection performance of the adaptive lasso depends on the weight used. It turns out that the weight assignment using the OLS estimate (OLS-adaptive lasso) can result in very poor… (More)

- Fuchang Gao, Ching-Kang Ing, Yuhong Yang
- Journal of Approximation Theory
- 2013

Consider `q-hulls, 0 < q ≤ 1, from a dictionary of M functions in L space for 1 ≤ p < ∞. Their precise metric entropy orders are derived. Sparse linear approximation bounds are obtained to characterize the number of terms needed to achieve accurate approximation of the best function in a `q-hull that is closest to a target function. Furthermore, in the… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 2001

|We study minimax-rate adaptive estimation for density classes indexed by continuous hyper-parameters. The classes are assumed to be partially ordered in terms of inclusion relationship. Under a mild condition on the minimax risks, we show that a minimax-rate adaptive estimator can be constructed for the classes. 1 Problem of interest This paper concerns… (More)

- Jinfeng Zhao, Huapeng Zhou, +7 authors Xueyong Li
- Plant, cell & environment
- 2016

Abscisic acid (ABA) is an important plant hormone integrating environmental stress and plant growth. Protein ubiquitination and deubiquitination are reversible processes catalysed by E3 ubiquitin ligase and deubiquitinating enzyme, respectively. Lots of E3 ubiquitin ligase and transcriptional factors modified by ubiquitination were reported to modulate ABA… (More)