报告标题问题：A Kernel Tweedie Compound Poisson Model
报告择要： The Tweedie GLM is a widely used method for predicting insurance premiums and loss reserving. However, the structure of the logarithmic mean is restricted to a linear form in the Tweedie GLM, which can be too rigid for many applications. The growing applications of Tweedie models motivate us to develop a much more flexible nonparametric Tweedie models in a reproducing kernel Hilbert space. The resulting estimator is called Ktweedie, which has multiple advantages over the classical Tweedie GLM by incorporating nonlinearity, nonadditivity, and complex interactions in the final estimator. We develop an efficient algorithm for solving the entire solution path of Ktweedie. Extensive simulations are conducted to show the very competitive finite sample performance of Ktweedie. We further demonstrate the application of Ktweedie by using rate making data and loss reserving data.
Lian Yi, 加拿大麦吉尔大先生物统计学博士，于麦吉尔大学获统计学和药理学本迷信位和风行病学硕士学位。首要研讨标的目的为高维统计、稀少统计进修、机械进修、凸优化，和统计方式在医疗安康、药物宁静、生物信息、保险精算等范畴的利用。具有生物统计阐发员及保险公司数据迷信家的练习履历。并在 EUROPEAN UROLOGY、Ecotoxicology And Environmental Safety 等国际着名期刊颁发多篇高品质文章。
报告标题问题：A sparse high dimensional generalized varying coefficient model for identifying genetic variants associated with regional methylation level
报告择要： Varying coefficient models offer the flexibility to learn the dynamic changes of regression coefficients. Despite their good interpretability and diverse applications, in high-dimensional settings, existing estimation methods for such models have important limitations. For example, we routinely encounter the need for variable selection when faced with a large collection of covariates with nonlinear/varying effects on outcomes, and no ideal solutions exist. One illustration of this situation could be identifying a subset of genetic variants with local influence on methylation levels in a regulatory region. To address this problem, we propose a composite sparse penalty that encourages both sparsity and smoothness for the varying coefficients. We present an efficient proximal gradient descent algorithm to obtain the penalized estimation of the varying regression coefficients in the model. A comprehensive simulation study has been conducted to evaluate the performance of our approach in terms of estimation, prediction and selection accuracy. We show that the inclusion of smoothness control yields much better results than having the sparsity-regularization only. Using an adaptive version of our penalty function, we can achieve notable additional performance gains. The method has been implemented in R package sparseSOMNiBUS available on GitHub
杨羿，现任加拿大麦吉尔大学 (McGill Uiniversity) 数学与统计学系副传授 (Associate Professor)，计较机系兼职传授，计量生物学名目成员，2008-2015年就读于美国明尼苏达大学，取得统计学博士学位，计较机与统计学双硕士学位。首要研讨范畴为统计机械进修与数据发掘，统计计较，高维统计揣度，及统计学方式在生物信息学，医学，精算学上的利用。已在Journal of the American Statistical Association、Biometrika等统计学顶级期刊颁发多篇高品质文章。