Maximum likelihood estimates of generalized gamma linear transformation model

Abstract

In this thesis, we build a new generalized gamma linear transformation model in survival analysis and apply it to the research of execution time of limit orders in stock market. In the past literature, accelerated failure time model and linear transformation model with given error distribution are popular models in survival analysis. However, the error distribution is usually unknown in reality. We choose to develop a new linear transformation model with the error term belonging to a generalized gamma distribution family, and propose an iterative algorithm to calculate the maximum likelihood estimates of the model. We conduct simulation studies to evaluate the finite sample performance of our model. We also fit our model to a real stock dataset of limit order execution times. Cross validation is carried out to illustrate the advantage of our model compared to accelerated failure time model.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology