報告題目：A Kernel Tweedie Compound Poisson Model
報告時間：2021年6月18日（星期五）09:00-10:00
騰訊會議：ID：268441154
報告摘要： 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.
報告人介紹：
Lian Yi, 加拿大麥吉爾大學生物統計學博士，于麥吉爾大學獲統計學和藥理學本科學位和流行病學碩士學位。主要研究方向為高維統計、稀疏統計學習、機器學習、凸優(yōu)化，以及統計方法在醫(yī)療健康、藥物安全、生物信息、保險精算等領域的應用。擁有生物統計分析員及保險公司數據科學家的實習經歷。并在 EUROPEAN UROLOGY、Ecotoxicology And Environmental Safety 等國際知名期刊發(fā)表多篇高質量文章。
報告題目：A sparse high dimensional generalized varying coefficient model for identifying genetic variants associated with regional methylation level
報告時間：2021年6月18日上午10：00-11：00
騰訊會議：ID：268441154
報告摘要： 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
報告人介紹：
楊羿，現任加拿大麥吉爾大學 (McGill Uiniversity) 數學與統計學系副教授 (Associate Professor)，計算機系兼職教授，計量生物學項目成員，2008-2015年就讀于美國明尼蘇達大學，獲得統計學博士學位，計算機與統計學雙碩士學位。主要研究領域為統計機器學習與數據挖掘，統計計算，高維統計推斷，及統計學方法在生物信息學，醫(yī)學，精算學上的應用。已在Journal of the American Statistical Association、Biometrika等統計學頂級期刊發(fā)表多篇高質量文章。
