Affiliations: [a] Statistics Discipline, Division of Sciences and Mathematics, University of Minnesota-Morris, Morris, MN, USA | [b] Department of Statistics, Cheongju University, Chungbuk, Korea
Correspondence:
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Corresponding author: Sunghae Jun, Department of Statistics, Cheongju University, Chungbuk 28503, Korea. E-mail: shjun@cju.ac.kr.
Abstract: Technology analysis is important work in management of technology. Most companies make plans for research and development (R&D) policy, new product development, or technological innovation using the results of technology analysis. In this paper, we propose a methodology of technology analysis using penalized regression models. We analyze the patent keywords extracted from the patent documents using ridge regression, least absolute shrinkage and selection operator, elastic net, and random forest. In addition, to show how our research could be applied to real problem efficiently, we carry out a case study of Apple technology. Our study contributes to perform R&D planning in technology management.
Keywords: Technology analysis, patent data analysis, zero-inflated problem, zero-inflated poisson model, zero-inflated negative binomial model, apple patent
