Penalized regression models for patent keyword analysis

Jong Min Kim, Jea Bok Ryu, Seung Joo Lee, Sunghae Jun

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    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.

    Original languageEnglish (US)
    Pages (from-to)239-244
    Number of pages6
    JournalModel Assisted Statistics and Applications
    Volume12
    Issue number3
    DOIs
    StatePublished - 2017

    Bibliographical note

    Publisher Copyright:
    © 2017 IOS Press and the authors.

    Keywords

    • Technology analysis
    • apple patent
    • patent data analysis
    • zero-inflated negative binomial model
    • zero-inflated poisson model
    • zero-inflated problem

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