@article{park_kim_lee_hands_rider_2019, title={Interdisciplinarity of Ph.D. students across the Atlantic. A Case of Interdisciplinary Research Team Building at the Student Level}, volume={22}, ISBN={1756-3062}, DOI={10.1080/14606925.2019.1594970}, abstractNote={This research explores the process of building an interdisciplinary design research team at the doctorate student level across institutions and disciplines. This study aims to establish a case addressing how to: 1) define aligned research goals, 2) outline overlapped approaches, such as methodologies, to achieve the research goals, and 3) organize a research team to conduct an interdisciplinary research project addressing overarching characteristics and research interests of members. This study was conducted in four phases: 1) understanding context, 2) framing inputs, 3) discussing processes (repeatable), and 4) analyzing outputs (products). Framed by Action Research, five data collection methods were used within the interdisciplinary team (participants) over two weeks. The interdisciplinary team building process, the benefits and shortcomings of the methods used, and the resulting research study with aligned research goals are presented in this paper.}, journal={DESIGN JOURNAL}, author={Park, Jinoh and Kim, Byungsoo and Lee, Boyeun and Hands, David and Rider, Traci Rose}, year={2019}, pages={1453–1466} }
 @article{li_lu_park_1999, title={Multivariate zero-inflated Poisson models and their applications}, volume={41}, ISSN={["0040-1706"]}, DOI={10.2307/1270992}, abstractNote={The zero-inflated Poisson (ZIP) distribution has been shown to be useful for modeling outcomes of manufacturing processes producing numerous defect-free products. When there are several types of defects, the multivariate ZIP (MZIP) model can be useful to detect specific process equipment problems and to reduce multiple types of defects simultaneously. This article proposes types of MZIP models and investigates distributional properties of an MZIP model. Finite-sample simulation studies show that, compared to the method of moments, the maximum likelihood method has smaller bias and variance, as well as more accurate coverage probability in estimating model parameters and zero-defect probability. Real-life examples from a major electronic equipment manufacturer illustrate how the proposed procedures are useful in a manufacturing environment for equipment-fault detection and for covariate effect studies.}, number={1}, journal={TECHNOMETRICS}, author={Li, CS and Lu, JC and Park, JH}, year={1999}, month={Feb}, pages={29–38} }
 @article{lu_park_yang_1997, title={Statistical inference of a time-to-failure distribution derived from linear degradation data}, volume={39}, ISSN={["0040-1706"]}, DOI={10.2307/1271503}, abstractNote={In the study of semiconductor degradation, records of transconductance loss or threshold voltage shift over time are useful in constructing the cumulative distribution function (cdf) of the time until the degradation reaches a specified level. In this article, we propose a model with random regression coefficients and a standard-deviation function for analyzing linear degradation data. Both analytical and empirical motivations of the model are provided. We estimate the model parameters, the cdf, and its quantiles by the maximum likelihood (ML) method and construct confidence intervals from the bootstrap, from the asymptotic normal approximation, and from inverting likelihood ratio tests. Simulations are conducted to examine the properties of the ML estimates and the confidence intervals. Analysis of an engineering dataset illustrates the proposed procedures.}, number={4}, journal={TECHNOMETRICS}, author={Lu, JC and Park, J and Yang, Q}, year={1997}, month={Nov}, pages={391–400} }