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- Yuhong Yang, Snedecor Hall
- 2004

We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under mild distributional assumptions on the future given the current outside information and the past observations. The risk bounds show that the combined forecast automatically achieves the best… (More)

- YUHONG YANG, DAN ZHU
- 2001

We study a multi-armed bandit problem in a setting where covariates are available. We take a nonparametric approach to estimate the functional relationship between the response (reward) and the covariates. The estimated relationships and appropriate randomization are used to select a good arm to play for a greater expected reward. Randomization helps… (More)

- Yuhong Yang
- 2003

It is well known that AIC and BIC have different properties in model selection. BIC is consistent in the sense that if the true model is among the candidates, the probability of selecting the true model approaches 1. On the other hand, AIC is minimax-rate optimal for both parametric and nonparametric cases for estimating the regression function. There are… (More)

- Yuhong Yang
- 2007

Adaptation over diierent procedures is of practical importance. Diierent procedures perform well under diierent conditions. In many practical situations, it is rather hard to assess which conditions are (approximately) satissed so as to identify the best procedure for the data at hand. Thus automatic adaptation over various scenarios is desirable. A… (More)

Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivity are explained, with particular emphasis on smoothing parameter selection. We review the existing literature in kernel regression, smoothing splines, wavelet regression, both for short-range and long-range… (More)

- Yuhong Yang, Y. YANG
- 2008

Theoretical developments on cross validation (CV) have mainly focused on selecting one among a list of finite-dimensional models (e.g., subset or order selection in linear regression) or selecting a smoothing parameter (e.g., bandwidth for kernel smoothing). However , little is known about consistency of cross validation when applied to compare between… (More)

- Yuhong Yang, Snedecor Hall
- 1998

Risk bounds are derived for regression estimation based on model selection over a unrestricted number of models. While a large list of models provides more exibility, sig-niicant selection bias may occur with bias-correction based model selection criteria like AIC. We incorporate a model complexity penalty term in AIC to handle the selection bias. Resulting… (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)

- Zheng Yuan, Yuhong Yang
- 2016

Model combining (mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been demonstrated in simulations and data examples, it is still unclear to a large extent when model combining should be preferred. In this work, firstly, an instability measure… (More)

- Zhuo Chen, Yuhong Yang
- 2004

This paper looks into the issue of evaluating forecast accuracy measures. In the theoretical direction, for comparing two forecasters, only when the errors are stochastically ordered, the ranking of the forecasts is basically independent of the form of the chosen measure. We propose well-motivated Kullback-Leibler Divergence based accuracy measures. In the… (More)