Cross-disciplinary research between machine learning and biostatistics based on curve estimation

作为机器学习和生物统计学之间的交叉学科研究，我一直在解决深化研究（理论研究），扩展研究（开发方法）和应用研究，并参考了给定的努力。在Deepened Research上发表了五篇论文，这是超出预期的进展。在应用研究方面，给出了一类拟共形映射的扩张量的渐近分布，并将其应用于胎儿数据的分析。LMS方法是一种有效的非线性回归方法，本文将其推广到非线性多元回归中。该方法被称为LMSR方法，并已被应用于分析人类胎儿数据。

As a cross-displinary research between machine learning and biostatistics, I have been addressing Deepened Research (Theoretical Research), Expanded Research(Developing Methodology), and Applied Research, with referring to the given effort. Five papers have been published in Deepened Research, which is the progress more than expected. I have published three papers in Expanded Research, so it certainly got the progress.In Applied Research, asymptotic distribution of dilatation of a certain quasi-conformal mapping was developed, which can be applied toanalysis of human fetus data. The LMS method, which is one of efficent nonlinear regression methods, has been extended tononlinear multivariate regression setting. The resultant method is called the LMSR method, and it has been applied to analysis of human fetus data.