Consistency of cross validation for comparing regression procedures (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...
PPAR Alpha Regulation of the Immune Response and Autoimmune Encephalomyelitis (2008)
Yuhong Yang, Anne R. Gocke, Amy Lovett-Racke, Paul D. Drew, Michael K. Racke
PPARs are members of the steroid hormone nuclear receptor superfamily and play an important role in the regulation of lipid metabolism, energy balance, artherosclerosis and glucose control. Recent...
Time Series Models for Forecasting: Testing or Combining? (2007)
In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select...
Time Series Models for Forecasting: Testing or Combining? (2007)
In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select...
Time Series Models for Forecasting: Testing or Combining? (2007)
In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select...
Time Series Models for Forecasting: Testing or Combining? (2007)
In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection (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...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection (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...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection (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...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection (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...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection (2004)
Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method was reported to perform relatively well....
Prediction/estimation With Simple Linear Models: Is It Really That Simple? (2004)
Consider the simple normal linear regression model for estimation/prediction at a new design point. When the slope parameter is not obviously nonzero, hypothesis testing and model selection methods...
Aggregating regression procedures to improve performance (2004)
A fundamental question regarding combining procedures concerns the potential gain and how much one needs to pay for it in terms of statistical risk. Juditsky and Nemirovski considered the case where...
Can the Strengths of AIC and BIC Be Shared? (2004)
It is well known that AIC and BIC have di#erent 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...
Time Series Models for Forecasting: Testing or Combining? (2002)
In this paper we compare forecasting performance of hypothesis testing procedures with a model combining algorithm called AFTER. Testing procedures are commonly used in practice to select a model...
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...
Regression with Multiple Candidate Models: Selecting or (2002)
Model averaging provides an alternative to model selection. An algorithm ARM rooted in information theory is proposed to combine different regression models/methods. A simulation is conducted in the...
Combining Different Procedures for Adaptive Regression (2002)
Given any countable collection of regression procedures (e.g., kernel, spline, wavelet, local polynomial, neural nets, etc), weshow that a single adaptive procedure can be constructed to share the...
Combining Time Series Models for Forecasting (2002)
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...
Combining Time Series Models for Forecasting (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...
Nonparametric regression with dependent errors (2001)
We study minimax rates of convergence for nonparametric regression under a random design with dependent errors. It is shown that when the errors are independent of the explanatory variables,...
Nonparametric Regressin with Correlated Errors (2001)
Opsomer, Jean, Wang, Yuedong, Yang, Yuhong
Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivity are explained, including the breakdown of several...
We study a multi-armed bandit problem in a setting with covariates available. We take a nonparametric approach to estimate the functional relationship between the response (reward) and the...
How Accurate Can Any Regression Procedure Be? (2000)
Various parametric and nonparametric regression procedures have been constructed according to different possible characteristics of the underlying regression function. To reduce the dependence on...
Model Selection for Nonparametric Regression (2000)
: 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 flexibility, significant selection bias...
Information-Theoretic Determination of Minimax Rates of Convergence (2000)
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...
Information-Theoretic Determination of Minimax Rates of Convergence (2000)
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...
On Adaptive Function Estimation (2000)
General results on adaptive function estimation are obtained with respect to a collection of estimation strategies for both density estimation and nonparametric regression under square L 2 loss. It...
Minimax Nonparametric Classification - Part I: Rates of Convergence (2000)
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...
Minimax Nonparametric Classification - Part II: Model Selection for Adaptation (2000)
We study nonparametric estimation of a conditional probability for classification based on a collection of finite-dimensional models. For the sake of flexibility, different types of models, linear or...
Nonparametric Regression and Prediction with Dependent Errors (2000)
We study minimax rates of convergence for nonparametric regression and prediction under a random design with dependent errors. It is shown that dependence among errors in general does not hurt a...
Adaptive Regression by Mixing (2000)
Adaptation over different procedures is of practical importance. Different procedures perform well under different conditions. In many practical situations, it is rather hard to assess which...
Minimax Nonparametric Classification - Part I: Rates of Convergence (2000)
This paper studies minimax aspects of nonparametric classification. Wefirst study minimax estimation of the conditional probability of a class label, given the feature variable. This function, say f#...
Minimax Rate Adaptive Estimation Over Continuous Hyper-Parameters (2000)
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...
Nonparametric Regression with Correlated Errors (2000)
Jean Opsomer, Yuedong Wang, Yuhong Yang
Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivity are explained, including the breakdown of several...
Combining Forecasting Procedures: Some Theoretical Results (2000)
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...
Adaptive Regression by Mixing (2000)
Adaptation over different procedures is of practical importance. Different procedures perform well under different conditions. In many practical situations, it is rather hard to assess which...
Regression with Multiple Candidate Models: Selecting or Mixing? (2000)
Model averaging provides an alternative to model selection. An algorithm ARM rooted in information theory is proposed to combine different regression models/methods. A simulation is conducted in the...
Nonparametric Regression and Prediction with Dependent Errors (2000)
We study minimax rates of convergence for nonparametric regression and prediction under a random design with dependent errors. It is shown that dependence among errors in general does not hurt a...
Minimax Nonparametric Classification - Part I: Rates of Convergence (2000)
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...
Mixing strategies for density estimation (2000)
General results on adaptive density estimation are obtained with respect to any countable collection of estimation strategies under Kullback-Leibler and squared $L_2$ losses. It is shown that without...
Minimax Rate Adaptive Estimation Over Continuous Hyper-Parameters (1999)
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...
Aggregating Regression Procedures for a Better Performance (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...
Combining Different Procedures for Adaptive Regression (1999)
Given any countable collection of regression procedures (e.g., kernel, spline, wavelet, local polynomial, neural nets, etc), we show that a single adaptive procedure can be constructed to share the...
Information-theoretic determination of minimax rates of convergence (1999)
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...
Mixing Strategies for Density Estimation (1999)
General results on adaptive density estimation are obtained with respect to any countable collection of estimation strategies under Kullback-Leibler and square L 2 losses. It is shown that without...
Nonparametric Regression with Correlated Errors (1999)
Jean Opsomer, Yuedong Wang, Yuhong Yang
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...
Information-Theoretic Determination of Minimax Rates of Convergence (1996)
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...
Adaptive Estimation in Pattern Recognition by Combining Different Procedures (1970)
: We study a problem of adaptive estimation of a conditional probability function in a pattern recognition setting. In many applications, for more flexibility, one may want to consider various...
Information-Theoretic Determination of Minimax Rates of Convergence (1970)
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...
Combining Forecasting Procedures: Some Theoretical Results.
We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under distributional assumptions on the...
Interlocked feedback loops contribute to the robustness of the Neurospora circadian clock
Cheng, Ping, Yang, Yuhong, Liu, Yi
Interlocked feedback loops may represent a common feature among the regulatory systems controlling circadian rhythms. The Neurospora circadian feedback loops involve white collar-1 (wc-1), wc-2, and...
Cheng, Ping, Yang, Yuhong, Gardner, Kevin H., Liu, Yi
In the frq-wc-based circadian feedback loops of Neurospora, two PAS domain-containing transcription factors, WHITE COLLAR-1 (WC-1) and WC-2, form heterodimeric complexes that activate the...
Cheng, Ping, Yang, Yuhong, Heintzen, Christian, Liu, Yi
The frequency (frq) gene, the central component of the frq-based circadian negative feedback loop, regulates various aspects of the circadian clock in Neurospora. However, the biochemical function of...
Regulation of the Neurospora circadian clock by casein kinase II
Yang, Yuhong, Cheng, Ping, Liu, Yi
Phosphorylation of clock proteins represents an important mechanism regulating circadian clocks. In Neurospora, clock protein FREQUENCY (FRQ) is progressively phosphorylated over time, and its level...
Functional conservation of light, oxygen, or voltage domains in light sensing
Cheng, Ping, He, Qiyang, Yang, Yuhong, Wang, Lixin, Liu, Yi
In Neurospora, the flavin adenine dinucleotide-containing protein WHITE COLLAR-1 is the blue-light photoreceptor for the circadian clock and other light responses. The putative chromophore-binding...
Yang, Yuhong, Cheng, Ping, He, Qiyang, Wang, Lixin, Liu, Yi
FREQUENCY (FRQ), a key component of the Neurospora circadian clock, is progressively phosphorylated after its synthesis. Previously, we identified casein kinase II (CKII) as a kinase that...
He, Qun, Cheng, Ping, Yang, Yuhong, He, Qiyang, Yu, Hongtao, Liu, Yi
Phosphorylation of the Neurospora circadian clock protein FREQUENCY (FRQ) regulates its degradation and the proper function of the clock. The mechanism by which FRQ undergoes degradation has not been...
Distinct roles for PP1 and PP2A in the Neurospora circadian clock
Yang, Yuhong, He, Qun, Cheng, Ping, Wrage, Philip, Yarden, Oded, Liu, Yi
Phosphorylation of the Neurospora circadian clock protein FREQUENCY by several kinases promotes its degradation and is important for the function of the circadian feedback loop. Here, we show that...
Interlocked feedback loops contribute to the robustness of the Neurospora circadian clock
Cheng, Ping, Yang, Yuhong, Liu, Yi
Interlocked feedback loops may represent a common feature among the regulatory systems controlling circadian rhythms. The Neurospora circadian feedback loops involve white collar-1 (wc-1), wc-2, and...
Cheng, Ping, Yang, Yuhong, Gardner, Kevin H., Liu, Yi
In the frq-wc-based circadian feedback loops of Neurospora, two PAS domain-containing transcription factors, WHITE COLLAR-1 (WC-1) and WC-2, form heterodimeric complexes that activate the...
Cheng, Ping, Yang, Yuhong, Heintzen, Christian, Liu, Yi
The frequency (frq) gene, the central component of the frq-based circadian negative feedback loop, regulates various aspects of the circadian clock in Neurospora. However, the biochemical function of...
Regulation of the Neurospora circadian clock by casein kinase II
Yang, Yuhong, Cheng, Ping, Liu, Yi
Phosphorylation of clock proteins represents an important mechanism regulating circadian clocks. In Neurospora, clock protein FREQUENCY (FRQ) is progressively phosphorylated over time, and its level...
Functional conservation of light, oxygen, or voltage domains in light sensing
Cheng, Ping, He, Qiyang, Yang, Yuhong, Wang, Lixin, Liu, Yi
In Neurospora, the flavin adenine dinucleotide-containing protein WHITE COLLAR-1 is the blue-light photoreceptor for the circadian clock and other light responses. The putative chromophore-binding...
Yang, Yuhong, Cheng, Ping, He, Qiyang, Wang, Lixin, Liu, Yi
FREQUENCY (FRQ), a key component of the Neurospora circadian clock, is progressively phosphorylated after its synthesis. Previously, we identified casein kinase II (CKII) as a kinase that...
He, Qun, Cheng, Ping, Yang, Yuhong, He, Qiyang, Yu, Hongtao, Liu, Yi
Phosphorylation of the Neurospora circadian clock protein FREQUENCY (FRQ) regulates its degradation and the proper function of the clock. The mechanism by which FRQ undergoes degradation has not been...
Distinct roles for PP1 and PP2A in the Neurospora circadian clock
Yang, Yuhong, He, Qun, Cheng, Ping, Wrage, Philip, Yarden, Oded, Liu, Yi
Phosphorylation of the Neurospora circadian clock protein FREQUENCY by several kinases promotes its degradation and is important for the function of the circadian feedback loop. Here, we show that...
He, Qun, Cha, Joonseok, He, Qiyang, Lee, Heng-Chi, Yang, Yuhong, Liu, Yi
The eukaryotic circadian oscillators consist of circadian negative feedback loops. In Neurospora, it was proposed that the FREQUENCY (FRQ) protein promotes the phosphorylation of the WHITE COLLAR...
LOCALIZED MODEL SELECTION FOR REGRESSION
Research on model procedure selection has focused on selecting a single model globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the...
COMBINING FORECASTING PROCEDURES: SOME THEORETICAL RESULTS
We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under distributional assumptions on the...
PREDICTION/ESTIMATION WITH SIMPLE LINEAR MODELS: IS IT REALLY THAT SIMPLE?
Consider the simple normal linear regression model for estimation prediction at a new design point. When the slope parameter is not obviously nonzero, hypothesis testing and information criteria can...
Time Series Models for Forecasting: Testing or Combining?
In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select...
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
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...
PPAR Alpha Regulation of the Immune Response and Autoimmune Encephalomyelitis
Yang, Yuhong, Gocke, Anne R., Lovett-Racke, Amy, Drew, Paul D., Racke, Michael K.
PPARs are members of the steroid hormone nuclear receptor superfamily and play an important role in the regulation of lipid metabolism, energy balance, artherosclerosis and glucose control. Recent...
A traditional approach to statistical inference is to identify the true or best model first with little or no consideration of the specific goal of inference in the model identification stage. Can...