Bayesian Count Data Modeling for Finding Technological Sustainability

Sunghae Jun
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Sunghae Jun: Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea

Sustainability, 2018, vol. 10, issue 9, 1-12

Abstract: Technology developments change society, and society demands new and innovative technology developments. We analyze technology to understand society and technology itself. Much research related to technology analysis has been introduced in various fields. Most of it has been on patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study a new patent analysis method based on the count data model and Bayesian regression analysis. Using the count data model, we analyzed the technological keywords extracted from the collected patent documents. We used the prior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we applied the proposed model to find sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domains, we carried out a case study using the patent documents related to artificial intelligence.

Keywords: count data; Bayesian regression; technological sustainability; Poisson probability distribution; patent analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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