Jiang, Wenxin "Michael" - Department of Statistics, Northwestern University

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Jiang, Wenxin "Michael" - Department of Statistics, Northwestern University

Group Sequential Procedures for Poisson Process Data with Frailty
LARGE TIME BEHAVIOR OF BOOSTING ALGORITHMS FOR REGRESSION AND CLASSIFICATION
Tweedie Class Loglinear Models for Longitudinal Data with Random Effects and
Some Theoretical Aspects of Boosting in the Presence of Noisy Data Wenxin Jiang wjiang@northwestern.edu
Hierarchical MixturesofExperts for Generalized Linear Models: Some Results on Denseness and Consistency
Manuscript Number: 1940 The VC Dimension for Mixtures of
On Weak Base Learners for Boosting Regression and Classification
Mixtures of marginal models By ORI ROSEN
HIERARCHICAL MIXTURESOFEXPERTS FOR EXPONENTIAL FAMILY REGRESSION MODELS
Manuscript Number: 1746 On the Approximation Rate of Hierarchical
Consistent Model Selection Based on Parameter Estimates November 16, 2002
Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error
Does Boosting Overfit: Views From an Exact Solution Wenxin Jiang
ON WEAK BASE HYPOTHESES AND THEIR IMPLICATIONS FOR BOOSTING REGRESSION AND CLASSIFICATION
Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error
Lecture Notes in Computer Science 1857---Proceedings of the First International Workshop on Multiple Classifier Systems, 8796, Springer, 2000.
Is Regularization Unnecessary for Boosting? Wenxin Jiang
On the Asymptotic Normality of Hierarchical MixturesofExperts for Generalized Linear Models
On the Identifiability of MixturesofExperts Wenxin Jiang and Martin A. Tanner \Lambday
STATISTICS 320-1 WINTER 2001 Statistical Methods I
MULTIVARIATE STATISTICAL METHODS Statistics D48-0 Instructor: Wenxin Jiang
Group Sequential Procedures for Poisson Process Data with Frailty
Tweedie Class Log-linear Models for Longitudinal Data with Random E ects and
On Consistency of Bayesian Inference with Mixtures of Logistic Regression
A note on some algorithms for the Gibbs posterior1 , Wenxin Jiangb2
Theory of Statistical Data Mining (Statistics 461 Section 21) Instructor: Wenxin Jiang
Wenxin Jiang Northwestern University
PROCESS CONSISTENCY FOR ADABOOST By Wenxin Jiang