Quantitative Model For Setting Manufacturer's Suggested Retail Price,
2020
Southern Methodist University
Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace
SMU Data Science Review
In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the …
Mapping Relationships And Positions Of Objects In Images Using Mask And Bounding Box Data,
2020
Southern Methodist University
Mapping Relationships And Positions Of Objects In Images Using Mask And Bounding Box Data, Jaime M. Villanueva Jr, Anantharam Subramanian, Vishal Ahir, Andrew Pollock
SMU Data Science Review
In this paper we present novel methods for automatically annotating images with relationship and position tags that are derived using mask and bounding box data. A Mask Region-based Convolutional Neural Network (Mask R-CNN) is used as the foundation for the ob- ject detection process. The relationships are found by manipulating the bounding box and mask segmentation outputs of a Mask R-CNN. The absolute positions, the positions of the objects relative to the image, and the relative positions, the positions of objects relative to the other objects, are then associated with the images as annotations that are out- put in order …
Gradient Boosting For Survival Analysis With Applications In Oncology,
2020
University of South Florida
Gradient Boosting For Survival Analysis With Applications In Oncology, Nam Phuong Nguyen
USF Tampa Graduate Theses and Dissertations
Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new …
Bayesian Analysis Of The Epsilon Skew Exponential Power Distribution,
2020
University of Arkansas Little Rock
Bayesian Analysis Of The Epsilon Skew Exponential Power Distribution, Michael Ghebremeskel Weldensea
Theses and Dissertations
The Epsilon Skew Exponential Power Distribution (ESEP) that was introduced by Elsalloukh et al. (2005) is an asymmetric distribution used for modeling asymmetric data. The ESEP includes Normal, Laplace, Epsilon Skew Normal (ESN), and Epsilon Skew Laplace (ESL) as particular cases, Elsalloukh et al. (2005). In the present study, since the ESEP distribution encompasses members with skewed and symmetric distributions, we perform and investigate the Bayesian analysis of this distribution using the methods of latent variables and uniform scale mixture for implementing the most common Markov chain Monte Carlo (MCMC) algorithm known as Gibbs sampling. Furthermore, we develop the posterior …
The Undergraduate Student’S Guide To Geometric Morphometrics,
2020
Pace University
The Undergraduate Student’S Guide To Geometric Morphometrics, Erika Crispo
Open Educational Resources
Embarking on a new research endeavor can be a daunting task. User guides, books, and published articles are written for an audience that already has some background experience in the field. Undergraduate students like you, who are at the very beginning of their research careers, often struggle to make sense of these documents. Furthermore, students like you often attempt to do so while balancing heavy course loads. Thus, I have written this document to help ease the burden so that you have more time to ponder the interesting scientific questions instead of digging through pages upon pages of documentation. I …
Playfair's Introduction Of Bar And Pie Charts To Represent Data,
2020
University of Colorado Denver
Playfair's Introduction Of Bar And Pie Charts To Represent Data, Diana White, River Bond, Joshua Eastes, Negar Janani
Statistics and Probability
No abstract provided.
Randomization-Based Confidence Intervals For Cluster Randomized Trials,
2020
Harvard University
Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang
Harvard University Biostatistics Working Paper Series
In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires randomization testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for …
Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes,
2020
Harvard TH Chan School of Public Health
Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes, Linda J. Harrison, Rui Wang
Harvard University Biostatistics Working Paper Series
Power calculation for stepped-wedge cluster randomized trials (SW-CRTs) presents unique challenges, beyond those of standard cluster randomized trials (CRTs), due to the need to consider temporal within cluster correlations and background period effects. To date, power calculation methods specific to SW-CRTs have primarily been developed under a linear model. When the outcome is binary, the use of a linear model corresponds to assessing a prevalence difference; yet trial analysis often employs a non-linear link function. We assess power for cross-sectional SW-CRTs under a logistic model fitted by generalized estimating equations. Firstly, under an exchangeable correlation structure, we show the power …
Markov Chain Epidemic Models And Parameter Estimation,
2020
Marshall University
Markov Chain Epidemic Models And Parameter Estimation, Oluwatobiloba Ige
Theses, Dissertations and Capstones
Over the years, various parts of the world have experienced disease outbreaks. Mathematical models are used to describe these outbreaks. We study the transmission of disease in simple cases of disease outbreaks by using compartmental models with Markov chains. First, we explore the formulation of compartmental SIS (Susceptible-Infectious-Susceptible) and SIR (Susceptible-Infectious-Recovered) disease models. These models are the basic building blocks of other compartmental disease models. Second, we build SIS and SIR disease models using both discrete and continuous time Markov chains. In discrete time models, transmission occurs at fixed time steps, and in continuous time models, transmission may occur at …
Sample Size Calculation And Blinded Recalculation For Analysis Of Covariance Models With Multiple Random Covariates,
2020
Paris Lodron University, Austria
Sample Size Calculation And Blinded Recalculation For Analysis Of Covariance Models With Multiple Random Covariates, Georg Zimmermann, Meinhard Kieser, Arne C. Bathke
Statistics Faculty Publications
When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.
Representing And Interpreting Data From Playfair,
2020
University of Colorado Denver
Representing And Interpreting Data From Playfair, Diana White, River Bond, Joshua Eastes, Negar Janani
Statistics and Probability
No abstract provided.
Association Between Baseline Abundance Of Peptoniphilus, A Gram-Positive Anaerobic Coccus, And Wound Healing Outcomes Of Dfus,
2020
University of Miami
Association Between Baseline Abundance Of Peptoniphilus, A Gram-Positive Anaerobic Coccus, And Wound Healing Outcomes Of Dfus, Kyung R. Min, Adriana Galvis, Katherine L. Baquerizo Nole, Rohita Sinha, Jennifer Clarke, Robert S. Kirsner, Dragana Ajdic
Department of Statistics: Faculty Publications
Diabetic foot ulcers (DFUs) lead to nearly 100,000 lower limb amputations annually in the United States. DFUs are colonized by complex microbial communities, and infection is one of the most common reasons for diabetes-related hospitalizations and amputations. In this study, we examined how DFU microbiomes respond to initial sharp debridement and off- loading and how the initial composition associates with 4 week healing outcomes. We employed 16S rRNA next generation sequencing to perform microbial profiling on 50 sam- ples collected from 10 patients with vascularized neuropathic DFUs. Debrided wound sam- ples were obtained at initial visit and after one week …
Representation Of Features As Images With Neighborhood Dependencies For Compatibility With Convolutional Neural Networks,
2020
Texas Tech University
Representation Of Features As Images With Neighborhood Dependencies For Compatibility With Convolutional Neural Networks, Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal
Department of Statistics: Faculty Publications
Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated …
Perceived Neighborhood: Preferences Versus Actualities,
2020
Department of Transportation and Natural Resouces, Travis County, Texas
Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh
Department of Statistics: Faculty Publications
Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …
In Praise Of Partially Interpretable Predictors,
2020
Mercer University
In Praise Of Partially Interpretable Predictors, Tri Le, Bertrand S. Clarke
Department of Statistics: Faculty Publications
Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smallermean-squared error or integratedmean-squared error.We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both …
Tumor Ablation Due To Inhomogeneous Anisotropic Diffusion In Generic Three-Dimensional Topologies,
2020
Texas Tech University
Tumor Ablation Due To Inhomogeneous Anisotropic Diffusion In Generic Three-Dimensional Topologies, Erdi Kara, Aminur Rahman, Eugenio Aulisa, Souparno Ghosh
Department of Statistics: Faculty Publications
In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal …
Statistical Downscaling With Spatial
Misalignment: Application To Wildland Fire
Pm2.5 Concentration Forecasting,
2020
North Carolina State University
Statistical Downscaling With Spatial Misalignment: Application To Wildland Fire Pm2.5 Concentration Forecasting, Suman Majumder, Yawen Guan, Brian J. Reich, Susan O’Neill, Ana G. Rappold
Department of Statistics: Faculty Publications
Fine particulate matter, PM2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM2.5 air pollution in the USA. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and …
The Effects Of Adverse Childhood Experiences On Behavioral Outcomes,
2020
University of Denver
The Effects Of Adverse Childhood Experiences On Behavioral Outcomes, Jennifer Thomas
Electronic Theses and Dissertations
This study intends to explore the intersection of two vulnerable populations, early childhood development and risks associated with exposure to adverse childhood experiences (ACEs). This study examines how age plays a role in the long-term relationship between ACEs and internal and external behaviors. This study seeks to answer the question of: How does age influence the relationship between number of ACEs and internal and external behaviors? The participants in this study include those aged 0 – 16 from the National Survey of Child and adolescent Well-Being (NSCAW) dataset. The NSCAW study consists of five waves of data where Wave I …
Is The Reliability Of Objective Originality Scores Confounded By Elaboration?,
2020
University of Denver
Is The Reliability Of Objective Originality Scores Confounded By Elaboration?, Shannon Marie Maio
Electronic Theses and Dissertations
The increased use of text-mining models as a scoring mechanism for divergent thinking (DT) tasks has sparked concerns about the ways in which automated Originality scores may be influenced by other dimensions of DT, especially Elaboration. The debate centers around the question of whether too much variance in automated Originality scores is accounted for by the number of words a participant uses in a response (i.e., Elaboration), and, thus, how the influence of Elaboration can affect the reliability of Originality scores. Here, a partial correlation analysis, in conjunction with text-mining and psychometric modeling, is conducted to test the degree to …
Measuring The Connective Action Of Black Lives Matter Activists: A Psychometric Investigation Into Twitter Data,
2020
University of Denver
Measuring The Connective Action Of Black Lives Matter Activists: A Psychometric Investigation Into Twitter Data, Paige Alfonzo
Electronic Theses and Dissertations
Many protest movements from the last twenty-first century have become increasingly networked and personalized. Several scholars have tapped into this change coining terms such as participatory action, digitally mediated action, computer-mediated communication, issue-based organization, and what I focus on in this project, connective action. Building on the ideas percolating across the literary landscape at the time, Bennett and Segerberg (2012) introduced the logic of connective action based on emergent characteristics they observed in post-2010 large-scale social movements. Both the logic of connective action and related work have become deeply ingrained in today's social movement scholarship. As such, I felt it …
