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Various Approaches on Parameter Estimation in Mixture and Non-Mixture Cure Models
Title
Various Approaches on Parameter Estimation in Mixture and Non-Mixture Cure Models.
Creator
Kutal, Durga Hari, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Analyzing life-time data with long-term survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or long-term survivors. In order to include the propor- tion of cure subjects, mixture and non-mixture cure models are considered. In this dissertation, we utilize both maximum likelihood and Bayesian methods to estimate model parameters. Simulation studies are carried out to verify the nite sample per-...
Show moreAnalyzing life-time data with long-term survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or long-term survivors. In order to include the propor- tion of cure subjects, mixture and non-mixture cure models are considered. In this dissertation, we utilize both maximum likelihood and Bayesian methods to estimate model parameters. Simulation studies are carried out to verify the nite sample per- formance of the estimation methods. Real data analyses are reported to illustrate the goodness-of- t via Fr echet, Weibull and Exponentiated Exponential susceptible distributions. Among the three parametric susceptible distributions, Fr echet is the most promising. Next, we extend the non-mixture cure model to include a change point in a covariate for right censored data. The smoothed likelihood approach is used to address the problem of a log-likelihood function which is not di erentiable with respect to the change point. The simulation study is based on the non-mixture change point cure model with an exponential distribution for the susceptible subjects. The simulation results revealed a convincing performance of the proposed method of estimation.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013083
Subject Headings
Survival Analysis., Bayesian statistical decision theory., Parameter estimation., Weibull distribution.
Format
Document (PDF)
Seagrass growth and survivorship under the influence of epiphyte grazers
Title
Seagrass growth and survivorship under the influence of epiphyte grazers.
Creator
Howard, Robert K., Short, F. T., Harbor Branch Oceanographic Institute
Date Issued
1986
PURL
http://purl.flvc.org/FCLA/DT/3353772
Subject Headings
Seagrasses--Florida--Indian River (Lagoon), Epiphytes, Grazing, Growth, Seagrasses, Survival
Format
Document (PDF)
Detection of multiple change-points in hazard models
Title
Detection of multiple change-points in hazard models.
Creator
Zhang, Wei, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Change-point detection in hazard rate function is an important research topic in survival analysis. In this dissertation, we firstly review existing methods for single change-point detection in piecewise exponential hazard model. Then we consider the problem of estimating the change point in the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one step...
Show moreChange-point detection in hazard rate function is an important research topic in survival analysis. In this dissertation, we firstly review existing methods for single change-point detection in piecewise exponential hazard model. Then we consider the problem of estimating the change point in the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one step further, we propose an counting process based and least squares based change-point detection algorithm. For single change-point case, consistency results are obtained. We then consider the detection of multiple change-points in the presence of long-term survivors via maximum likelihood based and counting process based method. Last but not least, we use a weighted least squares based and counting process based method for detection of multiple change-points with long-term survivors and covariates. For multiple change-points detection, simulation studies show good performances of our estimators under various parameters settings for both methods. All methods are applied to real data analyses.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004173
Subject Headings
Problem solving--Data processing., Process control--Statistical methods., Point processes., Mathematical statistics., Failure time data analysis--Data processing., Survival analysis (Biometry)--Data processing.
Format
Document (PDF)