Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents
Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents
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DOI：
10.1007/s11192-013-0953-4
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发表时间：
2013-07
期刊：
影响因子：
3.9
通讯作者：
F. Yoshikane
中科院分区：
文献类型：
--
作者：
F. Yoshikane
虽然已有很多研究阐明了影响“学术论文”被引频次的因素，但基于回归分析等统计分析对“专利”被引频次进行预测的研究很少。假设基于多种技术基础的专利往往是更经常被引用的重要专利，本研究考察了被引用专利分类数量的影响，并将其与发明人、分类、页数和权利要求书等其他因素进行了比较。使用这些因素进行多元线性，逻辑和零膨胀负二项回归分析。所有模型的被引专利分类数与被引频次均呈显著正相关。此外，多元回归分析表明，被引专利分类数对回归的贡献大于其他因素。这意味着，如果考虑到因素之间的混淆，则对前向引用的数量影响更大的是后向引用分类的多样性。
Although many studies have been conducted to clarify the factors that affect the citation frequency of “academic papers,” there are few studies where the citation frequency of “patents” has been predicted on the basis of statistical analysis, such as regression analysis. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines the influence of the number of cited patents’ classifications and compares it with other factors, such as the numbers of inventors, classifications, pages, and claims. Multiple linear, logistic, and zero-inflated negative binomial regression analyses using these factors are performed. Significant positive correlations between the number of classifications of cited patents and the citation frequency are observed for all the models. Moreover, the multiple regression analyses demonstrate that the number of classifications of cited patents contributes more to the regression than do other factors. This implies that, if confounding between factors is taken into account, it is the diversity of classifications assigned to backward citations that more largely influences the number of forward citations.