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Marginal analysis of panel counts through estimating functions.Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied in Wellner & Zhang (2000), assuming a Poisson counting process, and gives a nondecreasing estimator, which is the same as the nonparametric maximum likelihood estimator of Wellner & Zhang and thus is consistent without the Poisson assumption. Motivated by the construction of parametric generalized estimating equations, the second type is a set of data-adaptive quasi-score funct...
Source: Biometrika - June 28, 2009 Category: Biotechnology Authors: Hu XJ, Lagakos SW, Lockhart RA Tags: Biometrika Source Type: journals

Bayesian Nonparametric Functional Data Analysis Through Density Estimation.Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In many modern experimental settings, observations are obtained in the form of functions, and interest focuses on inferences on a collection of such functions. We propose a hierarchical model that allows us to simultaneously estimate multiple curves nonparametrically by using dependent Dirichlet Process mixtures of Gaussians to characterize the joint distribution of predictors and outcomes. Function estimates are then induced through the conditional distribution of the outcome given the predictors. The resulting approach allows for flexible estimation and clustering, while borrowing information across curves. We also s...
Source: Biometrika - March 7, 2009 Category: Biotechnology Authors: Rodríguez A, Dunson DB, Gelfand AE Tags: Biometrika Source Type: journals

Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data.Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation parameter of the semiparametric normal transformation model, which characterizes the bivariate dependence of bivariate survival o...
Source: Biometrika - December 1, 2008 Category: Biotechnology Authors: Li Y, Prentice RL, Lin X Tags: Biometrika Source Type: journals

Kernel stick-breaking processes.Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a class of kernel stick-breaking processes for uncountable collections of dependent random probability measures. The process is constructed by first introducing an infinite sequence of random locations. Independent random probability measures and beta-distributed random weights are assigned to each location. Predictor-dependent random probability measures are then constructed by mixing over the locations, with stick-breaking probabilities expressed as a kernel multiplied by the beta weights. Some theoretical properties of the process are described, including a covariate-dependent prediction rule. A retrospec...
Source: Biometrika - September 19, 2008 Category: Biotechnology Authors: Dunson DB, Park JH Tags: Biometrika Source Type: journals

Population intervention models in causal inference.Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
SummaryWe propose a new causal parameter, which is a natural extension of existing approaches to causal inference such as marginal structural models. Modelling approaches are proposed for the difference between a treatment-specific counterfactual population distribution and the actual population distribution of an outcome in the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population and therefore we refer to these models as population intervention models. We focus on intervention models estimating the effect of an intervention in terms of a difference ...
Source: Biometrika - August 19, 2008 Category: Biotechnology Authors: Hubbard AE, Laan MJ Tags: Biometrika Source Type: journals