a generalized heckman model is used for the joint modeling of longitudinal continuousresponses and dropout in order to see the influence of a small perturbation of the elements of thecovariance structure on displacement of the likelihood. the perturbation from random dropout in thedirection of informative dropout is considered for mastitis data.

Recursive credibility estimation is discussed from the viewpoint of linear filtering theory. A conjunction of geometric mterpretation and the innovation approach leads to general algorithms not developed before. Moreover, covariance characterizations considered by other researchers drop our elegantly as a result of geometric considerations. Examples are presented of Kalman type filters valid fo...

Choi, InKyung Ph.D., Purdue University, December 2014. Modeling spatial covariance functions. Major Professor: Hao Zhang. Covariance modeling plays a key role in the spatial data analysis as it provides important information about the dependence structure of underlying processes and determines performance of spatial prediction. Various parametric models have been developed to accommodate the id...

Recent research suggests that modeling coarticulation in speech is more appropriate at the syllable level. However, due to a number of additional factors that can affect the way syllables are articulated, creating multiple acoustic models per syllable might be necessary. Our previous research on longer-length multi-path models has proved that data-driven trajectory clustering to be an attractiv...

In this paper, low-complexity distributed fusion filtering algorithm for mixed continuous-discrete multisensory dynamic systems is proposed. To implement the algorithm a new recursive equations for local cross-covariances are derived. To achieve an effective fusion filtering the covariance intersection (CI) algorithm is used. The CI algorithm is useful due to its low-computational complexity fo...

Earth science studies deal in general with multivariate, regionalized, observations, which may be compositional, or not. Frequently, it is of interest to know whether those data have to be divided into different populations, a task usually performed by cluster analysis. This problem cannot be studied with traditional methods because samples are not independent. In that case, an extension of War...

Estimation of population covariance matrices from samples of multivariate data is important. (1) Estimation of principle components and eigenvalues. (2) Construction of linear discriminant functions. (3) Establishing independence and conditional independence. (4) Setting confidence intervals on linear functions. Suppose we observed p dimensional multivariate samples X1, X2, · · · , Xn i.i.d. wi...

A fast and exact procedure for the numerical synthesis of stationary multivariate Gaussian time series with a priori prescribed and well controlled autoand cross-covariance functions is proposed. It is based on extending the Circulant Embedding technique to the multivariate case and can be viewed as a modification and variation around the Chan and Wood algorithm proposed earlier to solve the sa...