Dynamic Preference Heterogeneity
報(bào)告時(shí)間： 2018年4月3日（周二）上午 10:00-11:30
報(bào)告地點(diǎn)： 計(jì)算所 703室
主講人： 李洋, 長(zhǎng)江商學(xué)院副教授
邀請(qǐng)人： 鄢貴海
報(bào)告摘要：
While much of the empirical marketing literature has focused on capturing cross-sectional heterogeneity, little research has been done on modeling the temporal evolution of heterogeneity. In this work, we develop a Bayesian non-parametric framework based on Hierarchical Gaussian Processes (HGP) for modeling dynamic heterogeneity, which flexibly captures both the evolution of population trends and individual-level departures from those trends over time. This novel specification allows for sharing of statistical information across individuals, and within individuals over time, to provide rich individual-level insights and efficient inferences regarding dynamics. In our application, we show robust evidence of dynamic heterogeneity across CPG categories during the Great Recession, and illustrate the clear gains from capturing dynamic heterogeneity through our HGP specification.
主講人簡(jiǎn)介：
李洋博士現(xiàn)任長(zhǎng)江商學(xué)院副教授。他本科畢業(yè)于北京大學(xué)電子學(xué)系，之后取得美國(guó) 哥倫比亞大學(xué)生物醫(yī)學(xué)工程碩士，哥倫比亞大學(xué)商學(xué)院博士。李洋專注于營(yíng)銷大數(shù) 據(jù)模型開(kāi)發(fā)和公司大數(shù)據(jù)戰(zhàn)略實(shí)現(xiàn)。李洋博士在營(yíng)銷數(shù)據(jù)模型、人工智能算法等方 面的研究成果已發(fā)表在Management Science, Marketing Science, Journal of Marketing Research等管理類國(guó)際頂級(jí)學(xué)術(shù)期刊上，并常在美國(guó)和歐洲的學(xué)術(shù)機(jī) 構(gòu)做關(guān)于數(shù)據(jù)模型的演講。在長(zhǎng)江商學(xué)院李洋講授EMBA、EE、FMBA和MBA等項(xiàng)目課程，曾為騰訊、百度、永輝超市、海爾等企業(yè)提供營(yíng)銷咨詢，并持有醫(yī)學(xué)圖像處理的美國(guó)專利。