Dependent censoring occurs in longitudinal studies of recurrent events when the censoring time depends on the potentially unobserved recurrent event times. To perform regression analysis in this setting, we propose a semiparametric joint model that formulates the marginal distributions of the...

In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error-prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method (...

In much of applied statistics variables of interest are measured with error. In particular, regression with covariates that are subject to measurement error requires adjustment to avoid biased estimates and invalid inference. We consider two aspects of this problem. Detection Limits (DL) arise...

Estimation of flow volumes in computer networks involves the use of data that are either highly aggregated or fairly noisy. We address several conceptual and practical aspects of the use of such data for flow volume estimation in this work. The results presented are often of general statistical...

This thesis develops methodology and asymptotic analysis for sparse estimators of the covariance matrix and the inverse covariance (concentration) matrix in high-dimensional settings. We propose estimators that are invariant to the ordering of the variables and estimators that exploit variable...

A standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest....

We consider the situation of two ordered categorical variables and a binary outcome variable, where one or both of the categorical variables may have missing values. The goal is to estimate the probability of response of the outcome variable for each cell of the contingency table of categorical...

The identification and estimation of a semiparametric simultaneous equation model with selectivity have been considered. The identification of structural parameters from reduced form parameters in the semiparametric model requires stronger conditions than the usual rank condition in the...

When data are missing at random, the missing-data mechanism can be ignored but this assumption is not always intuitive for general patterns of missing data. In part I, we consider maximum likelihood (ML) estimation for a non-ignorable mechanism which is called almost missing at random (AMAR). We...

The authors propose a robust transformation linear mixed-effects model for longitudinal continuous proportional data when some of the subjects exhibit outlying trajectories over time. It becomes troublesome when including or excluding such subjects in the data analysis results in different...