Statistical Inference Of Semiparametric Model Of Multi - Type Recurrence Event Data

In many application research region such as biomedicine, mechanical industry, demography and ecology, individuals may repeat experience many times of a certain event in a certain period of time which are we interested in, such as machine failure, with the onset of the disease, which was known as recurrent events in the survival analysis. Recurrent events data refers to the observation of individuals, which contain the recurrent time of interest. We can obtain single type recurrent data when we focus on single type event, and if we are interested in multiple types of recurrent events, and we can obtain those results of multi-type events data. Because there exist sequence and sequence between different events, and also with the existence of the censored time, leading to the structure of recurrent event data is usually very complex. More and more domestic and foreign scholars exert their effort to solve these difficulties and had gain many significant results, since 1980’s.According to recurrent event data, we are interested in assessing the effect of covariates on the recurrent events. There have been many kinds of regression models to be proposed to study the effect of covariates on the recurrent events. These models are mostly built based on two aspects, the intervals and the recurrent event data itself. In this thesis’ Charpterl we introduce some background knowledge and the concepts of recurrent events, and then introduce some basic definitions and knowledge of the model in survival analysis. In Chapter2 we summarize some results about recurrent events in recent research. Based on Lin et al in 2000’s models, we propose a model which can be used as a measure of average recurrent number of different event-type at time t, methods and asymptotic properties for inferences on this model are established and some results from simulation are presented.