Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of w...

Cancers of the breast and endometrium contribute substantially to mortality among women. Studies examining putative links between these cancers and estrogen have reported connicting results. The model presented here provides a natural framework for exploring the eeect of dependent covariate subclasses in explaining the variation in the response. Such subclass eeects can be investigated across s...

We introduce a new dimension-reduction technique, the Partially Linear Reduced-rank Regression (PLRR) model, for exploring possible nonlinear structure in a regression involving both multivariate response and covariate. The PLRR model specifies that the response vector loads linearly on some linear indices of the covariate, and nonlinearly on some other indices of the covariate. We give a set o...

Reference growth curves estimate the distribution of a measurement as it changes according to some covariate, often age. We present a new methodology to estimate growth curves based on mixture models and splines. We model the distribution of the measurement with a mixture of normal distributions with an unknown number of components, and model dependence on the covariate through the weights, usi...

In a designed experiment with covariates, experimental units are typically assigned to treatments randomly and analysis of covariance is used to account for the covariate. In cases where the covariate is known beforehand, the possibility exists to assign experimental units systematically to achieve a ”balanced” covariate distribution in each treatment. This balance can be accomplished by solvin...

A major assumption of the Cox proportional hazards model is that the effect of a given covariate does not change over time. If this assumption is violated, the simple Cox model is invalid, and more sophisticated analyses are required. This paper describes eight graphical methods for detecting violations of the proportional hazards assumption and demonstrates each on three published datasets wit...

Summary Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive is mostly limited situations where a correct model between the response covariates can be specified or method has well-understood properties...

assessment of the correlation between rock brittleness and rock fracture toughness has been the subject of extensive research works in the recent years. unfortunately, the brittleness measurement methods have not yet been standardized, and rock fracture toughness cannot be estimated satisfactorily by the proposed indices. in the present study, statistical analyses are performed on some data col...

one of the main distinctions between geomaterials and other engineering materials is the spatial variation of their properties in different directions. this characteristic of geomaterials -so called heterogeneity- is studied herewith. several spatial distributions are introduced to describe probabilistic variation of geotechnical properties of soils. among all, the absolute normal distribution ...

the purpose of this study is to compare the results of different probabilistic methods such as the perturbation method, stochastic finite element method (sfem) and monte carlo method. these methods were used to study the convergence of direct approach for slope stability analysis and are developed for a linear soil behavior. in this study, two dimensional random fields are used and both the fir...