报告题目 Title: Maximum smoothed likelihood for multivariate mixtures

报告人 Speaker：Prof.Michael Levine, Department of Statistics ,Purdue University

报告时间 Time：2015年6月23日（周二）下午3:00-4:00

报告地点 Place: 苏州大学本部精正楼二楼学术报告厅

报告简介 Abstract： We introduce an algorithm for estimating the parameters in a finite mixture of completely unspecified multivariate components in at least three dimensions under the assumption of conditionally independent coordinate dimensions. We prove that this algorithm, based on a majorization-minimization idea, possesses a desirable descent property just as any EM algorithm does. We discuss the similarities between our algorithm and a related one, the so-called nonlinearly smoothed EM algorithm for the non-mixture setting. We also demonstrate via simulation studies that the new algorithm gives very similar results to another algorithm that has been shown empirically to be effective but that does not satisfy any descent property.