@article{jeong_lu_zhou_ghosh_2007, title={Data-reduction method for spatial data using a structured wavelet model}, volume={45}, ISSN={["0020-7543"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-34247605442&partnerID=MN8TOARS}, DOI={10.1080/00207540600793547}, abstractNote={Recent advances in sensor instrumentation have provided opportunities for process engineers to collect data at various process steps in order to detect process problems and develop remedial procedures. This article presents a structured wavelet model for the reduction of two-dimensional data having distinct structures. The wavelet component of our model can handle irregular data patterns exhibiting many peaks and valleys, while the existence of a distinct data structure prompts the use of polynomial functions on wavelet coefficients. The two-dimensional antenna data is reduced with a structured wavelet model followed by some procedures for the detection of process defects based on the reduced-size data. A real-life example is presented to illustrate the usefulness of the proposed tools in detecting process problems from a potentially large volume of data exhibiting many peaks and valleys.}, number={10}, journal={INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH}, author={Jeong, Myong K. and Lu, Jye-Chyi and Zhou, Weixin and Ghosh, Sujit K.}, year={2007}, pages={2295–2311} }
 @article{lu_chen_zhou_2006, title={Quasi-lilkelihood estimation for GLM with random scales}, volume={136}, ISSN={["1873-1171"]}, DOI={10.1016/j.jspi.2004.06.028}, abstractNote={This paper uses random scales similar to random effects used in the generalized linear mixed models to describe “inter-location” population variation in variance components for modeling complicated data obtained from applications such as antenna manufacturing. Our distribution studies lead to a complicated integrated extended quasi-likelihood (IEQL) for parameter estimations and large sample inference derivations. Laplace's expansion and several approximation methods are employed to simplify the IEQL estimation procedures. Asymptotic properties of the approximate IEQL estimates are derived for general structures of the covariance matrix of random scales. Focusing on a few special covariance structures in simpler forms, the authors further simplify IEQL estimates such that typically used software tools such as weighted regression can compute the estimates easily. Moreover, these special cases allow us to derive interesting asymptotic results in much more compact expressions. Finally, numerical simulation results show that IEQL estimates perform very well in several special cases studied.}, number={2}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Lu, JC and Chen, D and Zhou, WX}, year={2006}, month={Feb}, pages={401–429} }