Browsing Statistics and Actuarial Science by Title
Now showing items 195-214 of 363
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Mitigating bias from intermittent measurement of time-dependent covariates in failure time analysis
(Wiley, 2020-06-15)Cox regression models are routinely fitted to examine the association between time-dependent markers and a failure time when analyzing data from clinical registries. Typically, the marker values are measured periodically ... -
A mixture model for bivariate interval-censored failure times with dependent susceptibility
(Springer, 2020-03-07)Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not ... -
Mixture Models for Coarsened Multivariate Failure Time Data
(University of Waterloo, 2018-08-13)The aim of this thesis is to develop statistical methodology for the analysis of life history data under incomplete observation schemes and with latent features which must be accom- modated to ensure models provide a ... -
A model for deceased-donor transplant queue waiting times
(Springer, 2015-01-01)In many jurisdictions, organ allocation is done on the basis of the health status of the patient, either explicitly or implicitly. This paper presents a self-promoting priority queueing model for patient waiting times which ... -
Modeling and Managing Longevity Risk: Models and Applications
(University of Waterloo, 2016-07-20)With the threat of longevity risk to the insurance industry becoming increasingly apparent in recent years, insurers and reinsurers are concerned about how to better model and manage longevity risk. However, modeling and ... -
Modeling and Prediction of Disease Processes Subject to Intermittent Observation
(University of Waterloo, 2016-07-21)This thesis is concerned with statistical modeling and prediction of disease processes subject to intermittent observation. Times of disease progression are interval-censored when progression status is only known at a ... -
Modeling Dynamic Network with Centrality-based Logistic Regression
(University of Waterloo, 2011-09-29)Statistical analysis of network data is an active field of study, in which researchers inves- tigate graph-theoretic concepts and various probability models that explain the behaviour of real networks. This thesis attempts ... -
Modelling Issues in Three-state Progressive Processes
(University of Waterloo, 2001)This dissertation focuses on several issues pertaining to three-state progressive stochastic processes. Casting survival data within a three-state framework is an effective way to incorporate intermediate events into an ... -
Mortality Prediction using Statistical Learning Approaches
(University of Waterloo, 2022-11-21)Longevity risk, as one of the major risks faced by insurers, has triggered a heated stream of research in mortality modeling among actuaries for effective design/pricing/risk management of insurance products. The idea of ... -
Multiple imputation for the analysis of incomplete compound variables
(Wiley, 2015-06)In many settings interest lies in modelling a compound variable defined as a function of two or more component variables. When one or more of the components are missing, the compound variable is not observed and a strategy ... -
Multiple testing using the posterior probability of half-space: application to gene expression data.
(University of Waterloo, 2005)We consider the problem of testing the equality of two sample means, when the number of tests performed is large. Applying this problem to the context of gene expression data, our goal is to detect a set of genes ... -
Multiply Robust Empirical Likelihood Inference for Missing Data and Causal Inference Problems
(University of Waterloo, 2019-06-06)Missing data are ubiquitous in many social and medical studies. A naive complete-case (CC) analysis by simply ignoring the missing data commonly leads to invalid inferential results. This thesis aims to develop statistical ... -
Multiscale GARCH Modeling and Inference
(University of Waterloo, 2018-10-11)The motivation behind this thesis is the shortage of formal statistical inference methods in the literature for testing whether a time series model is consistent with a sample at multiple sampling frequencies simultaneously. ... -
Multistate analysis from cross-sectional and auxiliary samples
(Wiley, 2020-02-20)Epidemiological studies routinely involve cross‐sectional sampling of a population comprised of individuals progressing through life history processes. We consider features of a cross‐sectional sample in terms of the ... -
A Multistate Model for Bivariate Interval-Censored Failure Time Data
(Wiley, 2008-12)Interval-censored life-history data arise when the events of interest are only detectable at periodic assessments. When interest lies in the occurrence of two such events, bivariate-interval censored event time data are ... -
Multistate Models for Biomarker Processes
(University of Waterloo, 2014-08-25)Multistate models are widely used for describing life history processes. In studies where individuals are observed continuously, the transition times between states are known exactly. However, when individuals are observed ... -
Multivariate First-Passage Models in Credit Risk
(University of Waterloo, 2008-10-17)This thesis deals with credit risk modeling and related mathematical issues. In particular we study first-passage models for credit risk, where obligors default upon first passage of a ``credit quality" process to ... -
Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models
(University of Waterloo, 2013-01-24)Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can ... -
Multivariate Multiscale Analysis of Neural Spike Trains
(University of Waterloo, 2013-12-17)This dissertation introduces new methodologies for the analysis of neural spike trains. Biological properties of the nervous system, and how they are reflected in neural data, can motivate specific analytic tools. Some of ... -
Multivariate Risk Measures for Portfolio Risk Management
(University of Waterloo, 2021-01-29)In portfolio risk management, the main foci are to control the aggregate risk of the entire portfolio and to understand the contribution of each individual risk unit in the portfolio to the aggregate risk. When univariate ...