Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 53 for “"Survival Models"”.
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Variable selection in discrete survival models
… model. However, variable selection for discrete survival analysis poses many challenges due to a complicated data structure. Survival data might have unobserved heterogeneity leading to biased estimates when not taken into account. Conventional variable selection methods have stability problems. …
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Alternative estimation methods for survival models
… the parameters of two types of three parameter survival models for the force of mortality, the Makeham model and the Weibull model. The existing methods for estimating these parameters by the method of least-squares include using a log-transformation on the force of mortality data. We will …
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Nuisance Parameter Estimation in Survival Models
… However, both alternatives require a conditional survival function as a nuisance parameter. This thesis focuses on the impact of the conditional survival function on the estimation of censored quantile regression and restricted means. In particular, we illustrate that a non-parametric estimator of …
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Mark-Recapture Creel Survey and Survival Models
… consider the analysis of a proportional hazards survival model for randomly censored observations, known as the Koziol-Green model. The model assumes that the lifetime survivor function is a power of the censored time survivor function.</p> <p>In Chapter 2, we introduce the model based approach …
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Approaches for Handling Time-Varying Covariates in Survival Models
Survival models are used in analysing time-to-event data. This type of data is very common in medical research. The Cox proportional hazard model is commonly used in analysing time-to-event data. However, this model is based on the proportional hazard (PH) assumption. Violation of this assumption …
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Advances in Differentially Methylated Region Detection and Cure Survival Models
… on two areas of statistics: DNA methylation and survival analysis. The first part of the dissertation pertains to the detection of differentially methylated regions in the human genome. The varying distribution of gaps between succeeding genomic locations, which are represented on the microarray …
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Bayesian analysis of spatial and survival models with applications of computation techniques
… methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with right-censored failure time. A conditional autoregressive (CAR) prior is used for the model to capture spatial effects. Markov chain Monte Carlo (MCMC) methods are used in the …
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Predicting the risk and trajectory of intensive care patients using survival models
… This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are …
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A comparison of some methods of modeling baseline hazard function in discrete survival models
The baseline parameter vector in a discrete-time survival model is determined by the number of time points. The larger the number of the time points, the higher the dimension of the baseline parameter vector which often leads to biased maximum likelihood estimates. One of the ways to overcome this …
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Discrete survival models with flexible link functions for age at first marriage among woman in Swaziland
… the use of exible link functions in discrete survival models through a simulation study and an application to the Swaziland Demographic and Health Survey (SDHS) data. The objective of the research study is to perform simulation exercises in order to compare the e ectiveness of di erent …
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Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
… censored where it reflects on the uncertainty of survival time until an event occur. Survival analysis can accommodates both fixed and time varying covariates with the presence of censored data. The survival time of parametric distribution of Weibull, exponential and log-logistic were derived by …
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A Comparison of Discrete and Continuous Survival Analysis
There has been confusion in choosing a proper survival model between two popular survival models of discrete and continuous survival analysis. This study aimed to provide empirical outcomes of two survival models in educational contexts and suggest a guideline for researchers who should adopt a …
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LIFE EXPECTANCY
… science is to phrase things in terms of survival models. There are popular survival functions that enable insurers to perform this calculation. With these functions, insurers are able to efficiently provide this service and ensure that life insurance will continue to be a thriving field …
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Advancements on the Interface of Computer Experiments and Survival Analysis
… surrogate modeling (e.g., Gaussian process models), and uncertainty quantification. Survival analysis focuses on modeling the period of time until a certain event happens. Data collection, prediction, and uncertainty quantification are also fundamental in survival models. In this …
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A Comparative Analysis of Machine Learning Models and Traditional Statistical Models for Continuous-Time Survival Analysis
Survival analysis is a statistical technique used to model time-to-event data, commonly applied in fields such as healthcare, engineering, and finance. Traditional approaches, including the Cox Proportional Hazards (CoxPH) model, have long been dominant due to their interpretability and theoretical …
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Essays on Corporate Default Prediction
… to apply a discrete transformation family of survival models to corporate default risk predictions. A class of Box-Cox transformations and logarithmic transformations are naturally adopted. The proposed transformation model family is shown to include the popular Shumway’s model and grouped …
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Performance analysis of garbage collection and dynamic reordering in a Lisp system
… and generation capacities. Analytic timing and survival models are used to represent garbage collection runtime and to derive structural results on its behavior. The survival model provides bounds on the age of objects surviving a garbage collection at a particular level. Empirical results show …
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Practice Patterns and Health Outcomes of Adjuvant Oxaliplatin Chemotherapy for Colorectal Cancer
… adjuvant chemotherapy improves survival for stage III colon cancer. Yet it is not without harms, most commonly dose-dependent peripheral neuropathy. The 2018 International Duration Evaluation of Adjuvant Chemotherapy (IDEA) trial sought to determine the optimal duration of …
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Vacancy durations in the office market
… data and as such can be examined using survival analysis. We present several parametric and non-parametric survival models. Four key characteristics -- unit size, asking rent, building height, and floor number -- are found significant across all model specifications. Specifically, …
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