THESIS
2016
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. C...[
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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.
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