Murrayand Tsiatis (1996) described a weighted survival estimate thatincorporates prognostic time-dependent covariate informationto increase the efficiency of estimation. We propose a test statisticbased on the statistic of Pepe and Fleming (1989, 1991) thatincorporates these weighted survival...

We propose a profile conditional likelihood approach to handle missing covariates in the general semiparametric transformation regression model. The method estimates the marginal survival function by the Kaplan-Meier estimator, and then estimates the parameters of the survival model and the...

The Cox (1972) regression model is extended to include discrete and mixed continuous/discrete failure time data by retaining the multiplicative hazard rate form of the absolutely continuous model. Application of martingale arguments to the regression parameter estimating function show the...

The main goal of this thesis is to use concepts and tools from extreme value theory (EVT) to model, make inference and develop prediction tools for the extremes of dependent data with a focus on the financial risk management applications. In the first part of the thesis, a new estimator of the...

Despite their widespread use as predictors of the spot price of oil, oil futures prices tend to be less accurate in the mean-squared prediction error sense than no-change forecasts. This result is driven by the variability of the futures price about the spot price, as captured by the oil futures...

Understanding the characteristics of traffic flows is crucial for allocating the necessary resources (bandwidth) to accommodate usersdemand. In this dissertation research, the problem of nonparametric and Moment-Based estimations of network flow characteristics basedon sampled flow data from...

We show that the effects of overfitting and underfitting a vector autoregressive (VAR) model are strongly asymmetric for VAR summary statistics involving higher-order dynamics (such as impulse response functions, variance decompositions, or long-run forecasts) . Underfit models often...

We compare and contrast several different methods for estimating the effect of treatment when responses are paired binomial observations. The ratio of binomial probabilities is the parameter of interest, while the binomial probabilities are nuisance parameters which may vary between pairs. The...

This research develops non-parametric methodology for sequential monitoring of paired time-to-event data when comparing years of life saved, or years where any unpleasant outcome is delayed, is of interest. The recommended family of test statistics uses integrated differences in survival...

A basic estimation strategy in sample surveys is to weight units inversely proportional to the probability of selection and response. Response weights in this method are usually estimated by the inverse of the sample-weighted response rate in an adjustment cell, that is, the ratio of the sum of...