Some New Estimator in Linear Mixed Models with Measurement error

Linear mixed models (LMMs) are an important tool for the analysis of a broad range of structures including longitudinal data, repeated measures data (including cross-over studies), growth and dose-response curve data, clustered (or nested) data, multivariate data, and correlated data. In many practical situations, the observation of variables is subject to measurement errors, and ignoring these in data analysis can lead to inconsistent parameter estimation and invalid statistical inference. Therefore, it is necessary to extend LMMs by taking the effect of measurement errors into account. Multicollinearity and fixed-effect variables with measurement errors are two well-known problems in the analysis of linear regression models. Although there exists a large amount of research on these two problems, there is by now no single technique superior to all other techniques for the analysis of regression models when these problems are present. In this thesis, we propose two new estimators using Nakamura's approach in LMM with measurement errors to overcome multicollinearity. We consider that prior information is available on fixed and random effects. The first estimator is the new mixed ridge estimator (NMRE) and the second estimator is the weighted mixed ridge estimator (WMRE). We investigate the asymptotic properties of these proposed estimators and compare the performance of them over the other estimators using the mean square error matrix (MSEM) criterion. Finally, a data example and a Monte Carlo simulation are also provided to show the theoretical results

Correlated Data Inference in Ontology Guided XML Security Engine

In this paper we examine undesired inference attacks from distributed public XML documents. An undesired inference is a chain of reasoning that leads to protected data of an organization using only publicly available information. We propose a framework, the Ontology guided XML Security Engine (Oxsegin), and algorithms to detect and prevent undesired inference attacks. Oxsegin uses the Correlated Inference Procedure to detect correlated information that may lead to undesired disclosure. The system operates on the DTD’s of XML documents, and uses an ontological class-hierarchy to identify tags that may contribute to security violations. A security violation pointer is assigned to a set of tags that may contribute to a possible security violation. The likelihood of a detected security violation is measured by a confidence level coefficient attached to the security violation pointers