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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)
Evaluating the effects of data collection methodology on the assessment of situations with the riverside situational q-sort
Title
Evaluating the effects of data collection methodology on the assessment of situations with the riverside situational q-sort.
Creator
Frascona, Richard, Sherman, Ryne A., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
The practice of evaluating situations with the Riverside Situational Q-Sort (RSQ:Wagerman & Funder, 2009) is relatively new. The present study aimed to investigate the theoretical framework supporting the RSQ with regards to the potential confounds of emotional state and the use of Likert-type ratings. Data were collected from a sample of Florida Atlantic University students (N = 206). Participants were primed for either a positive or negative mood state and asked to evaluate a situation with...
Show moreThe practice of evaluating situations with the Riverside Situational Q-Sort (RSQ:Wagerman & Funder, 2009) is relatively new. The present study aimed to investigate the theoretical framework supporting the RSQ with regards to the potential confounds of emotional state and the use of Likert-type ratings. Data were collected from a sample of Florida Atlantic University students (N = 206). Participants were primed for either a positive or negative mood state and asked to evaluate a situation with the RSQ in either the Q-Sort or Likert-type response format. Results suggested that response format has a significant influence on RSQ evaluations, but mood and the interaction between mood and response format do not. Exploratory analyses were conducted to determine the underlying mechanisms responsible.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004195, http://purl.flvc.org/fau/fd/FA00004195
Subject Headings
Sampling (Statistics), Statistical hypothesis testing--Methodology., Personality assessment--Methodology., Discourse analysis, Narrative., Riverside Situational Q-Sort.
Format
Document (PDF)
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications
Title
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications.
Creator
Lewkowitz, Stephanie, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient...
Show moreEvery scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and diā†µerent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004721, http://purl.flvc.org/fau/fd/FA00004721
Subject Headings
Computer vision in medicine, Diagnostic imaging -- Data processing, Mathematical models, Medical errors -- Prevention, Medical physics, Sampling (Statistics)
Format
Document (PDF)