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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)
Variances in meta-analysis
Title
Variances in meta-analysis.
Creator
Humphreys, Katherine L. B., Florida Atlantic University, Qian, Lianfen
Abstract/Description
Meta-analysis is a statistical method of combining many individual analyses. This thesis reviews the need for meta-analysis; the many statistical consideration facing the meta-analyst; and some of Hedges' results concerning the combined estimate of effect size with unequal weights from his 1981 and 1982 papers. Unequal weights used to combine estimates of effect size in meta-analysis are derived using the variances given by the large sample, normal approximation of the distribution of Hedges'...
Show moreMeta-analysis is a statistical method of combining many individual analyses. This thesis reviews the need for meta-analysis; the many statistical consideration facing the meta-analyst; and some of Hedges' results concerning the combined estimate of effect size with unequal weights from his 1981 and 1982 papers. Unequal weights used to combine estimates of effect size in meta-analysis are derived using the variances given by the large sample, normal approximation of the distribution of Hedges' unbiased estimates of effect sizes. These variances depend on the effect size and the sample sizes of both experimental and control groups. This creates circular definitions and calls for further estimates. This thesis analyzes the limiting normal approximation to derive a variance which is not dependent on effect size, and it provides guidelines for its use.
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Date Issued
1998
PURL
http://purl.flvc.org/fcla/dt/15544
Subject Headings
Meta-analysis, Social sciences--Statistical methods
Format
Document (PDF)
Discounting the role of causal attributions in the ANOVA model of attribution
Title
Discounting the role of causal attributions in the ANOVA model of attribution.
Creator
Hakala, Kori A., Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
For years attribution research has been dominated by the ANOVA model of behavior which proposes that people construct their dispositional attributions of others by carefully comparing and weighing all situational information using mental computations similar to the processes used by researchers to analyze data. A preliminary experiment successfully determined that participants were able to distinguish differences in variability assessed across persons (high vs. low consensus) and across...
Show moreFor years attribution research has been dominated by the ANOVA model of behavior which proposes that people construct their dispositional attributions of others by carefully comparing and weighing all situational information using mental computations similar to the processes used by researchers to analyze data. A preliminary experiment successfully determined that participants were able to distinguish differences in variability assessed across persons (high vs. low consensus) and across situations (high vs. low distinctiveness). Also, it was clear that the subjects could evaluate varying levels of situational constraint. A primary experiment administered to participants immediately following the preliminary study determined that participants grossly under-utilized those same variables when making dispositional attributions. Results gave evidence against the use of traditional ANOVA models and support for the use of the Behavior Averaging Principle of Attribution.
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Date Issued
2008
PURL
http://purl.flvc.org/FAU/166450
Subject Headings
Social sciences, Statistical methods, Analysis of variance, Data processing, Mathematical statistics, Attribution (Social psychology)
Format
Document (PDF)
Sensitivity analysis of predictive data analytic models to attributes
Title
Sensitivity analysis of predictive data analytic models to attributes.
Creator
Chiou, James, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we...
Show moreClassification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004274, http://purl.flvc.org/fau/fd/FA00004274
Subject Headings
Data mining, Forecasting -- Mathematical models, Social sciences -- Statistical methods, Ubiquitous computing
Format
Document (PDF)