This research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear... Show moreThis research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear analysis. Further, it demonstrates that making assumptions, reducing the data, or simplifying the problem results in negative effect on the outcomes. This study utilizes the U.S. Patent Inventor database and the Medical Innovation dataset. Initially, we employ time-series models to enhance the quality of the results for event history analysis (EHA), add insights, and infer meanings, explanations, and conclusions. Then, we introduce newer algorithms of machine learning and machine learning with a time-to-event element to offer more robust methods than previous papers and reach optimal solutions by removing assumptions or simplifications of the problem, combine all data that encompasses the maximum knowledge, and provide nonlinear analysis. Show less
