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.

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Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.