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KALMAN FILTER MODEL
Title
KALMAN FILTER MODEL.
Creator
Agiakloglou, Christos N., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis demonstrates the theory and the application of the Kalman filter model, a model where the coefficients are not assumed to be constant over time but time-varying. This model is approached in two different ways. The first is the recursive approach and the second is the Mixed estimation approach. Both of these two approaches describe in different ways the original Kalman filter model. This thesis also contains an empirical application of the Kalman filter model, using real data from...
Show moreThis thesis demonstrates the theory and the application of the Kalman filter model, a model where the coefficients are not assumed to be constant over time but time-varying. This model is approached in two different ways. The first is the recursive approach and the second is the Mixed estimation approach. Both of these two approaches describe in different ways the original Kalman filter model. This thesis also contains an empirical application of the Kalman filter model, using real data from the Greek economy to estimate the Demand for Money.
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Date Issued
1987
PURL
http://purl.flvc.org/fcla/dt/14372
Subject Headings
Kalman filtering, Estimation theory
Format
Document (PDF)
RIDGE REGRESSION
Title
RIDGE REGRESSION.
Creator
VELENTZAS, KONSTANTINOS THEODOROS., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis demonstrates the theory and logic of the ridge regression method of statistical estimation. The effect of multicollinearity on ordinary least squares is explored, and the optimality of the ridge estimators is derived. The critical analyses of ridge regression that have been developed in the biased estimation literature and by Bayesian statisticians is discussed. The technique of ridge regression is compared to ordinary least squares in the context of estimating price and income...
Show moreThis thesis demonstrates the theory and logic of the ridge regression method of statistical estimation. The effect of multicollinearity on ordinary least squares is explored, and the optimality of the ridge estimators is derived. The critical analyses of ridge regression that have been developed in the biased estimation literature and by Bayesian statisticians is discussed. The technique of ridge regression is compared to ordinary least squares in the context of estimating price and income elasticities for Greek imports.
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Date Issued
1984
PURL
http://purl.flvc.org/fcla/dt/14202
Subject Headings
Ridge regression (Statistics), Estimation theory
Format
Document (PDF)
OPTIMIZATION OF DATA ACQUISITION IN OPTICAL TOMOGRAPHY BASED ON ESTIMATION THEORY
Title
OPTIMIZATION OF DATA ACQUISITION IN OPTICAL TOMOGRAPHY BASED ON ESTIMATION THEORY.
Creator
Javidan, Mahshad, Pashaie, Ramin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
In any experimental platform, data acquisition is the first and essential step, and occasionally the most time-consuming and costly operation. During the process of data acquisition, we conduct experiments to measure the response of the system to a set of inputs. Methods of optimal design of experiment can be used to determine the most informative measurements and avoid numerous traps that trial-and-error experimentation might cause. In this research, we have developed a general approach for...
Show moreIn any experimental platform, data acquisition is the first and essential step, and occasionally the most time-consuming and costly operation. During the process of data acquisition, we conduct experiments to measure the response of the system to a set of inputs. Methods of optimal design of experiment can be used to determine the most informative measurements and avoid numerous traps that trial-and-error experimentation might cause. In this research, we have developed a general approach for designing optimal experiments, subsequently applying it to the domain of optical tomography. Optical tomography is a vital technology that enables three-dimensional imaging by reconstructing images from two-dimensional projections. This technology has applications across various fields, including medicine and material science. The process involves two main phases: data acquisition and image reconstruction. The traditional raster scanning method has been the standard approach for data acquisition, but it presents challenges in terms of scanning speed, quality, and exposure to harmful radiations in some cases. This has prompted researchers to explore ways to optimize the scanning process.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014350
Subject Headings
Optical tomography, Data Collection, Estimation theory
Format
Document (PDF)
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)
COMPARISON OF COMPUTER-BASED INSTRUCTIONAL METHODS FOR IMPROVING ESTIMATES OF MIXTURES PROBLEMS
Title
A COMPARISON OF COMPUTER-BASED INSTRUCTIONAL METHODS FOR IMPROVING ESTIMATES OF MIXTURES PROBLEMS.
Creator
Evans, Arthur C., Florida Atlantic University
Abstract/Description
The present study evaluates the effectiveness of three instructional methods, labeled Examples, Graph, and Discovery, for improving students' estimates of mixture problems. The problems involved mixing various quantities of acid and water solutions. The student's task was to estimate the percentage of acid in the resultant mixture. Although none of the three methods caused a significant change in estimation accuracy, the results were in the predicted direction, suggesting that the methods may...
Show moreThe present study evaluates the effectiveness of three instructional methods, labeled Examples, Graph, and Discovery, for improving students' estimates of mixture problems. The problems involved mixing various quantities of acid and water solutions. The student's task was to estimate the percentage of acid in the resultant mixture. Although none of the three methods caused a significant change in estimation accuracy, the results were in the predicted direction, suggesting that the methods may become more effective with improvements. The results also revealed a significant difference between males' and females' overall performances. Further analysis suggested that this difference may reflect differential understanding of the underlying concepts on which the problems were based. Suggestions for improving the present instructional methods are made, as well as alternative approaches for improving students' estimates.
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Date Issued
1984
PURL
http://purl.flvc.org/fcla/dt/14203
Subject Headings
Computer-assisted instruction, Estimation theory--Computer programs
Format
Document (PDF)
Determination of probability density from statistical moments by neural network approach
Title
Determination of probability density from statistical moments by neural network approach.
Creator
Zheng, Zhiyin., Florida Atlantic University, Cai, Guo-Qiang, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
It is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with...
Show moreIt is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with error back-propagation training algorithm is proposed for the purpose and the parametric method is adopted for identifying the probability density function. Three examples are given to illustrate the applicability of the approach. All three examples show that the neural network approach gives quite accurate results in comparison with either the exact or simulation ones.
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Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/15330
Subject Headings
Distribution (Probability theory), Moments method (Statistics), Estimation theory, Structural failures--Investigation, Neural networks (Computer science)
Format
Document (PDF)
Effects of gene selection and data sampling on prediction of breast cancer treatments
Title
Effects of gene selection and data sampling on prediction of breast cancer treatments.
Creator
Heredia, Brian, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in...
Show moreIn recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in the studies to analyze the datasets and their effects on treatment prediction with a particular interest in the selection of genes for predicting patient response. We will also analyze the datasets on our own in a uniform manner to determine the validity of these datasets in terms of learning potential and provide strategies for future work which explore how to best identify gene signatures.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004292, http://purl.flvc.org/fau/fd/FA00004292
Subject Headings
Antineoplastic agents -- Development, Breast -- Cancer -- Treatment, Cancer -- Genetic aspects, DNA mircroarrays, Estimation theory, Gene expression
Format
Document (PDF)
The Use Of Overlapping Vs. Non-Overlapping Moving Block Bootstrapping To Estimate The Variance Of A Statistic Of Dependent Data
Title
The Use Of Overlapping Vs. Non-Overlapping Moving Block Bootstrapping To Estimate The Variance Of A Statistic Of Dependent Data.
Creator
Davis, Benjamin F., Radulovic, Dragan, Florida Atlantic University
Abstract/Description
Determining the variance of a statistic (such as the sample median) can be difficult. Various methods of Bootstrapping (re-sampling with replacement) were used to estimate variance of one or more statistics based on a single sample. This estimator was compared to the empirical estimators based on repeated simulations of various sample sizes from a given distribution. Of particular interest was which of the methods of Bootstrapping were most effective with a dependent data set. Different...
Show moreDetermining the variance of a statistic (such as the sample median) can be difficult. Various methods of Bootstrapping (re-sampling with replacement) were used to estimate variance of one or more statistics based on a single sample. This estimator was compared to the empirical estimators based on repeated simulations of various sample sizes from a given distribution. Of particular interest was which of the methods of Bootstrapping were most effective with a dependent data set. Different degrees of dependency were used for the simulations with dependent data.
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Date Issued
2008
PURL
http://purl.flvc.org/fau/fd/FA00000738
Subject Headings
Bootstrap (Statistics), Social sciences--Statistical methods, Mathematical statistics, Sampling (Statistics), Estimation theory, Nonparametric statistics
Format
Document (PDF)