arXiv subjects

Myungjin Kim

Publications and source records attributed to Myungjin Kim.

4 recordsLinked to original sources

arXiv2026

Improved identification of breakpoints in piecewise regression and its applications

Identifying breakpoints in piecewise regression is critical in enhancing the reliability and interpretability of data fitting. In this paper, we propose novel algorithms based on the greedy algorithm to accurately and efficiently identify breakpoints in piecewise polynomial regression. The algorithm updates the breakpoints to minimize the error by exploring the neighborhood of each breakpoint. It has a fast convergence rate and stability to find optimal breakpoints. Moreover, it can determine the optimal number of breakpoints. The computational results for real and synthetic data show that its accuracy is better than any existing methods. The real-world datasets demonstrate that breakpoints through the proposed algorithm provide valuable data information.

stat.ML↗

arXiv2024

I Can Embrace and Avoid Vagueness Myself: Supporting the Design Process by Balancing Vagueness through Text-to-Image Generative AI

This study examines the role of vagueness in the design process and its strategic management for the effective human-AI interaction. While vagueness in the generation of design ideas promotes diverse interpretations and prevents fixation, excessive vagueness can lead to scattered results. Designers attempt to use image search tools or generative AIs (e.g., Dall-E) for their work but often fail to achieve satisfactory results because the level of vagueness is not properly managed in these technologies. In this work, we identified how designers coordinate vagueness in their design process and applied key components of the process to the design of CLAY, an interactive system that balances vagueness through iterative prompt refinement by integrating the strengths of text-to-image generative AI. Results from our user study with 10 fashion designers showed that CLAY effectively supported their design process, reducing design time, and expanding creative possibilities compared to their existing practice, by allowing them to both embrace and avoid vagueness as needed. Our study highlights the importance of identifying key characteristics of the target user and domain, and exploring ways to incorporate them into the design of an AI-based interactive tool.

cs.HC↗

arXiv2020

Spatiotemporal Dynamics, Nowcasting and Forecasting of COVID-19 in the United States

Epidemic modeling is an essential tool to understand the spread of the novel coronavirus and ultimately assist in disease prevention, policymaking, and resource allocation. In this article, we establish a state of the art interface between classic mathematical and statistical models and propose a novel space-time epidemic modeling framework to study the spatial-temporal pattern in the spread of infectious disease. We propose a quasi-likelihood approach via the penalized spline approximation and alternatively reweighted least-squares technique to estimate the model. Furthermore, we provide a short-term and long-term county-level prediction of the infected/death count for the U.S. by accounting for the control measures, health service resources, and other local features. Utilizing spatiotemporal analysis, our proposed model enhances the dynamics of the epidemiological mechanism and dissects the spatiotemporal structure of the spreading disease. To assess the uncertainty associated with the prediction, we develop a projection band based on the envelope of the bootstrap forecast paths. The performance of the proposed method is evaluated by a simulation study. We apply the proposed method to model and forecast the spread of COVID-19 at both county and state levels in the United States.

stat.AP↗

arXiv2020

Comparing and Integrating US COVID-19 Data from Multiple Sources with Anomaly Detection and Repairing

Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus.

stat.AP↗