Handling Ambiguity of Interval Censored Events

Censored data refers to a type of data where the specific timing of events of
interest remains unknown, and we only have information about their occurrence
within a specified interval. This type of data is encountered in various fields such
as medicine, finance, criminology, and politics. However, drawing inferences
from censored data is challenging due to increased uncertainty. In our study, we
focused on three significant aspects related to interval-censored data: change
point detection, intensity estimation, and event forecasting. The objective of
my research is to address the inherent uncertainty in this data by employing
statistical learning and deep learning techniques, with the aim of providing
practical insights and answers to relevant questions. The subsequent sections
provide a concise overview of each topic we investigated.
• Change point detection involves identifying the time points at which there
are shifts or alterations in the underlying model. However, the presence
of interval-censored data renders traditional methods unsuitable for this
task. In our research, we introduced a novel approach by combining the
joinpoint model with Bayesian Model Averaging (BMA) to detect a se-
quence of significant changes. We applied this proposed model to analyze
the 2020 presidential approval rate, aiming to identify the events that
influenced shifts in public sentiment.
• Intensity estimation is a widely explored research area; however, the in-
troduction of interval-censored data presents a new challenge in this field.
Most existing work primarily concentrates on time-to-event analysis within
the context of survival analysis, which makes it challenging to extend those
approaches to scenarios where the concept of time-to-event is not appli-
cable. In our study, we specifically focused on penalized temporal inten-
sity estimation, which offers a framework that yields precise and practical
estimates of intensity while accounting for the unique characteristics of
interval-censored data. This approach provides both accurate and realis-
tic estimates that are valuable in practical applications.
• The framework introduced in the previous project lacks the ability to
capture abrupt changes in the underlying process if they occur. To ad-
dress this limitation, we put forward a deep learning-based framework that
enables event forecasting using historical data, thereby facilitating the de-
tection of sudden changes and trends. This approach proves particularly
valuable in fields such as criminology, where accurate crime intensity fore-
casts can aid in effectively combating the upward trajectory of crime rates
and implementing preventive measures.
By undertaking these endeavors, we have successfully showcased the practical
applications of data science and its impact on our daily lives. This research has
contributed significantly to the advancement of our understanding of modeling
interval-censored data. Through our work, we have made notable strides in
this field, paving the way for improved methodologies and insights that can be
applied in various domains.