Model Selection And Survival Analysis With Application To Large Time Varying Networks
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Model Selection and Survival Analysis with Application to Large Time-varying Networks
Survival models have been applied to time-to-event data for a long time, and usually a number of covariates are assumed to influence the distribution of the time to event through the model. The Cox proportional hazard model is commonly used in this context. To have a parsimonious model without losing consistency inestimation, several authors have extended the variable selection techniques of Fan and Li (2001) to survival settings. For example, the variable selection problem for the Cox model is studied in Fan and Li (2002). Recently, survival models like the Cox model are also extended to apply to dynamic network data (Vu et al., 2011b; Perry and Wolfe, 2013), where the observations are dependent. In this dissertation, we study the variable selection problem for a survival model other than the Cox model. In addition, we extend the variable selection work to the dynamic network model setting.We first discuss the problem of variable selection for the proportional odds model, an alternative to Cox's model, and show how to maximize the penalized profile likelihood to estimate parameters and select variables simultaneously. Using a novel application of the semi-parametric theory developed by Murphy and Van der Vaart (2000), we derive asymptotic properties of the resulting estimators, including consistency results and the oracle property. In addition, we propose algorithms to maximize the penalized likelihood estimator based on a majorization-minimization (MM) algorithm. Tests on simulated and real data sets demonstrate that the newlyproposed algorithm performs well in practice.Next, we extend the penalization idea to the Cox model in an egocentric approach to dynamic networks, and select covariates by maximizing the penalized partial likelihood function. Asymptotic properties of both the unpenalized and penalized partial likelihood estimates are developed under certain regularity conditions. We also implement the estimation and test the prediction performance of these estimates in a citation network. Since the covariates are time-varying, the computation cost is high. After variable selection, the model is reduced, which simplifies the calculation for future predictions. Another method to reduce the computational complexity is to use the case-control approximation, in which instead of using all the at-risk nodes in the network, only a subset is sampled to evaluate the partial likelihood function. By using this approximation, the computation time is shortened dramatically, while the prediction performance is still satisfactory in the citation network.
Proceedings of Fourth International Conference on Computing and Communication Networks
This book includes selected peer-reviewed papers presented at fourth International Conference on Computing and Communication Networks (ICCCN 2024), held at Manchester Metropolitan University, UK, during 17–18 October 2024. The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber physical systems, data analytics, cyber security for industry 4.0, and smart and sustainable environmental systems.
Handbook of Bayesian Variable Selection
Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material