Sampling (Statistics)

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
degrees of dependency were used for the simulations with dependent data.

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 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 di↵erent 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.

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 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.