Gibbs posterior for variable selection in high-dimensional classification and data mining (2008)
Jiang, Wenxin, Tanner, Martin A.
In the popular approach of "Bayesian variable selection" (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction...
Bayesian variable selection has gained much empirical success recently in a variety of applications when the number $K$ of explanatory variables $(x_1,...,x_K)$ is possibly much larger than the...
Locally Adaptive Nonparametric Binary Regression (2007)
Wood, Sally, Kohn, Robert, Cottet, Remy, Jiang, Wenxin, Tanner, Martin
A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions...
As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some...
As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some...
As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some...
As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some...
The Indirect Method: Inference Based on Intermediate Statistics—A Synthesis and Examples (2004)
Jiang, Wenxin, Turnbull, Bruce
This article presents an exposition and synthesis of the theory and some applications of the so-called indirect method of inference. These ideas have been exploited in the field of econometrics, but...
Process consistency for AdaBoost (2004)
Recent experiments and theoretical studies show that AdaBoost can overfit in the limit of large time. If running the algorithm forever is suboptimal, a natural question is how low can the prediction...
Discussions of boosting papers, and rejoinders (2004)
Bartlett, Peter L., Bickel, Peter J., Bühlmann, Peter, Freund, Yoav, Friedman, Jerome, Hastie, Trevor, ...
Discussions of: "Process consistency for AdaBoost" [Ann. Statist. 32 (2004), no. 1, 13-29] by W. Jiang; "On the Bayes-risk consistency of regularized boosting methods" [ibid., 30-55] by G. Lugosi and...
Indirect inference for survival data (2003)
Turnbull, Bruce W., Jiang, Wenxin
In this paper we describe the so-called “indirect” method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated...
Thesis (LL. M.)--University of Washington, 2003.
Submitted to Professor Donald C. Clarke, Law 600-A, June 2003.
Consistent Model Selection Based on Parameter Estimates (2002)
Wenxin Jiang, Xiangyang Liu, Johnson Johnson
We consider model selection based on estimators that are asymptotically normal. Such a method can be applied to the context of estimating equations, since a complete specification of the probability...
On weak base Hypotheses and their implications for boosting regression and classification (2002)
When studying the training error and the prediction error for boosting, it is often assumed that the hypotheses returned by the base learner are weakly accurate, or are able to beat a random guesser...
Some Theoretical Aspects of Boosting in the Presence of Noisy Data (2001)
This is a survey of some theoretical results on boosting obtained from an analogous treatment of some regression and classification boosting algorithms. Some related papers include [J99] and...
Some Theoretical Aspects of Boosting in the Presence of Noisy Data (2001)
This is a survey of some theoretical results on boosting obtained from an analogous treatment of some regression and classification boosting algorithms. Some related papers include [J99] and...
Does Boosting Overfit: Views From an Exact Solution (2000)
We consider the AdaBoost algorithm using the piecewise constant base hypotheses on the predictor space [0; 1]. The boosted solutions are not unique, and one exact solution after a sufficiently large...
Process Consistency for AdaBoost (2000)
Introduction. Some recent experimental results [e.g., Friedman, Hastie and Tibshirani (1999); Grove and Schuurmans (1998); Mason et al. (1998)] and theoretical examples [Jiang (1999)] suggest that...
Is Regularization Unnecessary for Boosting? (2000)
this paper we present examples where `boosting forever ' leads to suboptimal predictions; while some regularization method, on the other hand, can achieve asymptotic optimality, at least in theory....
On Weak Base Hypotheses And Their Implications For Boosting Regression And Classification (2000)
this paper we solve the problem in
On Weak Base Learners for Boosting Regression and Classification (2000)
The most basic property of the boosting algorithm is its ability to reduce the training error, subject to the critical assumption that the base learners generate weak hypotheses that are better that...
Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification (2000)
. One basic property of the boosting algorithm is its ability to reduce the training error, subject to the critical assumption that the base learners generate `weak' (or more appropriately, `weakly...
Wenxin Jiang, Victor Kipnis, Douglas Midthune, Raymond J. Carroll
this paper that this is not the case for local estimation procedures, where the components in a given parameterization are to be modeled by local polynomials. We show that the bias that arises from...
Bayesian and Frequentist Inference for Ecological Inference: the (2000)
Ori Rosen, Wenxin Jiang, Gary King, Martin A. Tanner
In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R Theta C contingency tables, including a covariate. The proposed Bayesian model extends the...
Mixtures of Marginal Models (2000)
Ori Rosen, Wenxin Jiang, Martin A. Tanner
this paper, we adapt a mixture model originally developed for regression models with independent data for the more general case of correlated outcome data, which includes longitudinal data as a...
On the Asymptotic Normality of Hierarchical Mixtures-of-Experts for Generalized Linear Models (1999)
Wenxin Jiang, Martin A. Tanner
In the class of hierarchical mixtures-of-experts (HME) models, "experts" in the exponential family with generalized linear mean functions of the form /(ff + x T fi) are mixed, according to a set of...
This paper intends to focus on a commonly-used class of non-linear models, namely the log-linear models. Thall and Veil (1990) proposed a log-linear model for repeated counts in which two types of...
On the Identifiability of Mixtures-of-Experts (1999)
Wenxin Jiang, Martin A. Tanner
In mixtures-of-experts (ME) models, "experts" of generalized linear models are combined, according to a set of local weights called the "gating function". The invariant transformations of the ME...
Jiang, Wenxin, Tanner, Martin A.
We consider hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form $\psi(\alpha + \mathbf{x}^T \mathbf{\beta})$...
Point Process Regression Models for Multiple Events with Random Effects and Measurement Error (1999)
Wenxin Jiang, Bruce W. Turnbull, Larry C. Clark
Statistical methodology is presented for the analysis of multiple events with random effects and measurement error. We model multiple events in a general space using a random measure, and define...
Bruce W. Turnbull, Wenxin Jiang, Larry C. Clark
Statistical methodology is presented for the statistical analysis of nonlinear measurement error models. Our approach is to provide adjustments for the usual maximum likelihood estimators, their...
The VC Dimension for Mixtures of Binary Classifiers (1999)
The mixtures-of-experts (ME) methodology provides a tool of classification when experts of logistic regression models or Bernoulli models are mixed according to a set of local weights. We show that...
Wenxin Jiang, Martin A. Tanner
this paper we consider the denseness and consistency of these models in the generalized linear model context. Before proceeding we present some notation regarding mixtures and hierarchical mixtures...
Wenxin Jiang, Bruce W. Turnbull, Larry C. Clark
Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our...
Group Sequential Procedures for Poisson Process Data with Frailty (1999)
This paper investigates group sequential procedures for recurrent events data, allowing frailty (see Oakes, 1992), or the random heterogeneity of event frequencies among different subjects (see...
Wenxin Jiang, Martin A. Tanner
We investigate a class of hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form /(ff + x T fi) are mixed. Here...
On the Approximation Rate of Hierarchical Mixtures-of-Experts for Generalized Linear Models (1998)
Wenxin Jiang, Martin A. Tanner
We investigate a class of hierarchical mixtures-of-experts (HME) models where generalized linear models with nonlinear mean functions of the form /(ff + x T fi) are mixed. Here /(Delta) is the...
Thesis (Ph.D.)--Cornell University, August, 1996.
Regression Analysis of Mean Lifetime: Exploring Nonlinear Relationship with Heteroscedasticity
As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some...
Evidence for the existence of a robust pattern of prey selection in food webs
Stouffer, Daniel B, Camacho, Juan, Jiang, Wenxin, Nunes Amaral, Luís A
Food webs aim to provide a thorough representation of the trophic interactions found in an ecosystem. The complexity of empirical food webs, however, is leading many ecologists to focus dynamic...