Current Search: Bayesian statistical decision theory (x)
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 Title
 Bayesian econometrics: Analysis and illustrations.
 Creator
 Giakoumatos, Gerasimos S., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
 Abstract/Description

This thesis presents the theory and an application of Bayesian econometrics. The classical theory of econometrics is also presented for a comparison study. In the Bayesian case the theory of the prior information, which is the distinguishing characteristic of the Bayesian approach, is presented by considering the cases of informative and noninformative priors. The classical and Bayesian approach represent the two fundamental, although opposite in the concept of probability, schools of...
Show moreThis thesis presents the theory and an application of Bayesian econometrics. The classical theory of econometrics is also presented for a comparison study. In the Bayesian case the theory of the prior information, which is the distinguishing characteristic of the Bayesian approach, is presented by considering the cases of informative and noninformative priors. The classical and Bayesian approach represent the two fundamental, although opposite in the concept of probability, schools of thought in statistics and econometrics. An application to the estimation of standard macroeconomic equations is also included where both classical and Bayesian techniques are employed.
Show less  Date Issued
 1988
 PURL
 http://purl.flvc.org/fcla/dt/14484
 Subject Headings
 Bayesian statistical decision theory
 Format
 Document (PDF)
 Title
 Bayesian approach to an exponential hazard regression model with a change point.
 Creator
 Abraha, Yonas Kidane, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

This thesis contains two parts. The first part derives the Bayesian estimator of the parameters in a piecewise exponential Cox proportional hazard regression model, with one unknown change point for a right censored survival data. The second part surveys the applications of change point problems to various types of data, such as longterm survival data, longitudinal data and time series data. Furthermore, the proposed method is then used to analyse a real survival data.
 Date Issued
 2014
 PURL
 http://purl.flvc.org/fau/fd/FA00004013
 Subject Headings
 Bayesian statistical decision theory, Mathematical statistics, Multivariate analysis  Data processing
 Format
 Document (PDF)
 Title
 Various Approaches on Parameter Estimation in Mixture and NonMixture Cure Models.
 Creator
 Kutal, Durga Hari, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

Analyzing lifetime data with longterm survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or longterm survivors. In order to include the propor tion of cure subjects, mixture and nonmixture 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 lifetime data with longterm survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or longterm survivors. In order to include the propor tion of cure subjects, mixture and nonmixture 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 goodnessof 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 nonmixture 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 loglikelihood function which is not di erentiable with respect to the change point. The simulation study is based on the nonmixture 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.
Show less  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)
 Title
 Population genetic structure and evolutionary history of North Atlantic beluga whales (Delphinapterus leucas) from West Greenland, Svalbard and the White Sea.
 Creator
 O'CorryCrowe, Gregory, Lydersen, C., HeideJørgensen, M. P., Hansen, Lauren, Mukhametov, L. M., Dove, O., Kovacs, K. M., Harbor Branch Oceanographic Institute
 Date Issued
 2010
 PURL
 http://purl.flvc.org/FCLA/DT/3166887
 Subject Headings
 Beluga, White whale Baffin Bay (North Atlantic Ocean), Microsatellites (Genetics), Nucleotide sequence, Bayesian statistical decision theory
 Format
 Document (PDF)
 Title
 Classification of software quality using Bayesian belief networks.
 Creator
 Dong, Yuhong., Florida Atlantic University, Khoshgoftaar, Taghi M.
 Abstract/Description

In today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as faultprone or not faultprone. A general...
Show moreIn today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as faultprone or not faultprone. A general classification rule was applied to yield classification Bayesian Belief Network models. Six classification Bayesian Belief Network models were developed based on quality metrics data records of two very large window application systems. The fit data set was used to build the model and the test data set was used to evaluate the model. The first two models used median based data cluster technique, the second two models used median as critical value to cluster metrics using Generalized Boolean Discriminant Function and the third two models used KolniogorovSmirnov test to select the critical value to cluster metrics using Generalized Boolean Discriminant Function; All six models used the product metrics (FAULT or CDCHURN) as predictors.
Show less  Date Issued
 2002
 PURL
 http://purl.flvc.org/fcla/dt/12918
 Subject Headings
 Computer softwareQuality control, Software measurement, Bayesian statistical decision theory
 Format
 Document (PDF)
 Title
 A min/max algorithm for cubic splines over kpartitions.
 Creator
 Golinko, Eric David, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

The focus of this thesis is to statistically model violent crime rates against population over the years 19602009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression...
Show moreThe focus of this thesis is to statistically model violent crime rates against population over the years 19602009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression coefficients based upon the cubic spline model and the knots chosen by the minimum/maximum algorithm. We then utilize the best subsets regression method to aid in model selection in which we find the minimum value of the Bayesian Information Criteria. Finally, we preent the R2adj as a measure of overall goodness of fit of our selected model. We have found among the fifty states and Washington D.C., 42 out of 51 showed an R2adj value that was greater than 90%. We also present an overall model of the United States. Also, we show additional applications our algorithm for data which show a non linear association. It is hoped that our method can serve as a unified model for violent crime rate over future years.
Show less  Date Issued
 2012
 PURL
 http://purl.flvc.org/FAU/3342107
 Subject Headings
 Spline theory, Data processing, Bayesian statistical decision theory, Data processing, Neural networks (Computer science), Mathematical statistics, Uncertainty (Information theory), Probabilities, Regression analysis
 Format
 Document (PDF)
 Title
 Predictive modeling for wellness.
 Creator
 Pulumati, Pranitha, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
 Abstract/Description

Wellness and healthy life are the most common concerns for an individual to lead a happy life. A webbased approach known as Wellness Scoring is being developed taking into people’s concerns for their health issues. In this approach, four different classifiers are being investigated to predict the wellness. In this thesis, we investigated four different classifiers (a probabilistic graphical model, simple probabilistic classifier, probabilistic statistical classification and an artificial...
Show moreWellness and healthy life are the most common concerns for an individual to lead a happy life. A webbased approach known as Wellness Scoring is being developed taking into people’s concerns for their health issues. In this approach, four different classifiers are being investigated to predict the wellness. In this thesis, we investigated four different classifiers (a probabilistic graphical model, simple probabilistic classifier, probabilistic statistical classification and an artificial neural network) to predict the wellness outcome. An approach to calculate wellness score is also addressed. All these classifiers are trained on real data, hence giving more accurate results. With this solution, there is a better way of keeping track of an individuals’ health. In this thesis, we present the design and development of such a system and evaluate the performance of the classifiers and design considerations to maximize the end user experience with the application. A user experience model capable of predicting the wellness score for a given set of risk factors is developed.
Show less  Date Issued
 2014
 PURL
 http://purl.flvc.org/fau/fd/FA00004321, http://purl.flvc.org/fau/fd/FA00004321
 Subject Headings
 Bayesian statistical decision theory, Expert systems (Computer science), Health risk assessment, Medicine, Preventive, Patient self monitoring, Self care, Health, Well being
 Format
 Document (PDF)