题目：Semiparametric

inference for multiple nonnegative distributions with excess zero observations

时间：12月7日，上午10:00

地点：博识楼434

A non-standard, but not

uncommon, situation is to observe multiple samples of nonnegative data which

have a high proportion of zeros. This talk will focus on some important, and

fundamental, statistical inference problems for such data structure. A unique

feature of the target populations is that the distribution of each sample is

characterized by a non-standard mixture of a singular distribution at zero and

a skewed nonnegative component. We propose modelling the nonnegative components

using a semiparametric, multiple-sample, density ratio model. Under this semiparametric

setup, we can exploit information from all available samples even with

unspecified underlying distributions. The first part of this talk studies the

problem of testing homogeneity of multiple such distributions. We develop a new

empirical likelihood ratio (ELR) test for homogeneity and show that this ELR

has a $\chi^2$-type limiting distribution under the homogeneous null

hypothesis. A nonparametric bootstrap procedure is further proposed to

calibrate the finite sample distribution of the ELR. The consistency of this

bootstrap procedure is established under both the null and alternative

hypotheses. The second part of this talk investigates the problem of making

inference on the means of multiple such distributions. We develop a new ELR

statistic, and show that this ELR has a $\chi^2$-type limiting distribution

under a general null hypothesis. This result allows us to construct a new test

for mean equality as an important special case. Some simulation and real data

analysis results will also be presented.

个人简介：王淳林，厦门大学经济学院助理教授，2017年1月获加拿大滑铁卢大学统计学博士学位。王老师于2017年2月至8月在滑铁卢大学从事研究员工作，同年8月加盟厦门大学经济学院。他的主要研究方向比较广泛，主要有经验似然，bootstrap抽样方法，实验对照数据，半参数与非参数似然推断方法，带有不等式约束的统计推断，极值理论等，他尤其在带有零的数据领域内有较深的造诣。