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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
- 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)

- Minhui Paik, Yuhong Yang
- Statistical applications in genetics and…
- 2004

Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when… (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, Hui Zou
- 2001

Statistical models (e.g., ARIMA models) have been commonly used in time series data analysis and forecasting. Typically one model is selected based on a selection criterion (e.g., AIC), hypothesis testing, and/or graphical inspections. The selected model is then used to forecast future values. However, model selection is often unstable and may cause an… (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)

- 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)

- Yuhong Yang
- 1999

Model averaging provides an alternative to model selection. An algorithm ARM rooted in information theory is proposed to combine diierent regression models/methods. A simulation is conducted in the context of linear regression to compare its performance with familiar model selection criteria AIC and BIC, and also with some Bayesian model averaging (BMA)… (More)