Measuring Sexual Excitation And Sexual Inhibition In A Dutch-Speaking Sample,
2020
University of Arkansas, Fayetteville
Measuring Sexual Excitation And Sexual Inhibition In A Dutch-Speaking Sample, Malachi Willis
Graduate Theses and Dissertations
Background: Individual differences in sexual excitation and sexual inhibition are important predictors of sexual functioning. Psychometric instruments for these aspects of sexual response were originally developed separately for men (Sexual Inhibition /Sexual Excitation Scales [SIS/SES]) and women (Sexual Excitation/Sexual Inhibition Inventory for Women [SESII-W]). These measures were then adapted to function similarly in samples comprising both men and women (Sexual Inhibition/Sexual Excitation Scales-Short Form [SIS/SES-SF] and Sexual Excitation/Sexual Inhibition Inventory for Women and Men [SESII-W/M], respectively). No published study to our knowledge has administered the SIS/SES and SESII-W/M questionnaires to a sample of both women and men. In the present …
Research In Short Term Actuarial Modeling,
2020
California State University, San Bernardino
Research In Short Term Actuarial Modeling, Elijah Howells
Electronic Theses, Projects, and Dissertations
This paper covers mathematical methods used to conduct actuarial analysis in the short term, such as policy deductible analysis, maximum covered loss analysis, and mixtures of distributions. Assessment of a loss variable's distribution under the effect of a policy deductible, as well as one with an implemented maximum covered loss, and under both a policy deductible and maximum covered loss will also be covered. The derivation, meaning, and use of cost per loss and cost per payment will be discussed, as will those of an aggregate sum distribution, stop loss policy, and maximum likelihood estimation. For each topic, special cases …
A Study Of Cusum Statistics On Bitcoin Transactions,
2020
CUNY Hunter College
A Study Of Cusum Statistics On Bitcoin Transactions, Ivan Perez
Theses and Dissertations
In this thesis, our objective is to study the relationship between transaction price and volume in the BTC/USD Coinbase exchange. In the second chapter, we develop a consecutive CUSUM algorithm to detect instantaneous changes in the arrival rate of market orders. We begin by estimating a baseline rate using the assumption of a local time-homogeneous Poisson process. Our observations lead us to reject the plausibility of a time-homogeneous Poisson model on a more global scale by using a chi squared test. We thus proceed to use CUSUM-based alarms to detect consecutive upward and downward changes in the arrival rate of …
Sensitivity Analysis For Incomplete Data And Causal Inference,
2020
Southern Methodist University
Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen
Statistical Science Theses and Dissertations
In this dissertation, we explore sensitivity analyses under three different types of incomplete data problems, including missing outcomes, missing outcomes and missing predictors, potential outcomes in \emph{Rubin causal model (RCM)}. The first sensitivity analysis is conducted for the \emph{missing completely at random (MCAR)} assumption in frequentist inference; the second one is conducted for the \emph{missing at random (MAR)} assumption in likelihood inference; the third one is conducted for one novel assumption, the ``sixth assumption'' proposed for the robustness of instrumental variable estimand in causal inference.
Evaluation Of The Utility Of Informative Priors In Bayesian Structural Equation Modeling With Small Samples,
2020
Southern Methodist University
Evaluation Of The Utility Of Informative Priors In Bayesian Structural Equation Modeling With Small Samples, Hao Ma
Education Policy and Leadership Theses and Dissertations
The estimation of parameters in structural equation modeling (SEM) has been primarily based on the maximum likelihood estimator (MLE) and relies on large sample asymptotic theory. Consequently, the results of the SEM analyses with small samples may not be as satisfactory as expected. In contrast, informative priors typically do not require a large sample, and they may be helpful for improving the quality of estimates in the SEM models with small samples. However, the role of informative priors in the Bayesian SEM has not been thoroughly studied to date. Given the limited body of evidence, specifying effective informative priors remains …
“The Prediction Of Fantasy Football”,
2020
Louisiana Tech University
“The Prediction Of Fantasy Football”, Chelsea Robinson
Mathematics Senior Capstone Papers
In this paper, we consider the game fantasy football, which allows people to simulate being a National Football League team owner. Imaginary owners select from the best players in the NFL and compete on weekly basis based upon player performances on the field. Fantasy football has become popular over the years. In 2011, according to the Fantasy Sports Trade Association there were 35 million people that played fantasy sports online in the United States and Canada. The most major companies that use fantasy football are Yahoo, ESPN, and NFL, even though there are more platforms. Many people use these platforms …
Can Auxiliary Information Improve Rasch Estimation At Small Sample Sizes?,
2020
James Madison University
Can Auxiliary Information Improve Rasch Estimation At Small Sample Sizes?, Derek Sauder
Dissertations, 2020-current
The Rasch model is commonly used to calibrate multiple choice items. However, the sample sizes needed to estimate the Rasch model can be difficult to attain (e.g., consider a small testing company trying to pretest new items). With small sample sizes, auxiliary information besides the item responses may improve estimation of the item parameters. The purpose of this study was to determine if incorporating item property information (i.e., characteristics of the items related to item difficulty) in a random effects linear logistic test model (RE-LLTM) would improve estimation of item difficulty. A simulation study was conducted that varied sample size, …
Propensity Score Matching And Generalized Boosted Modeling In The Context Of Model Misspecification: A Simulation Study,
2020
James Madison University
Propensity Score Matching And Generalized Boosted Modeling In The Context Of Model Misspecification: A Simulation Study, Briana G. Craig
Masters Theses, 2020-current
In the absence of random assignment, researchers must consider the impact of selection bias – pre-existing covariate differences between groups due to differences among those entering into treatment and those otherwise unable to participate. Propensity score matching (PSM) and generalized boosted modeling (GBM) are two quasi-experimental pre-processing methods that strive to reduce the impact of selection bias before analyzing a treatment effect. PSM and GBM both examine a treatment and comparison group and either match or weight members of those groups to create new, balanced groups. The new, balanced groups theoretically can then be used as a proxy for the …
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation.,
2020
University of Louisville
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya
Electronic Theses and Dissertations
Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …
Introduction To Research Statistical Analysis: An Overview Of The Basics,
2020
HCA Healthcare
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
HCA Healthcare Journal of Medicine
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach,
2020
Southern Methodist University
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring,
2020
Kennesaw State University
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang
Doctor of Data Science and Analytics Dissertations
In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals,
2020
Western Kentucky University
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals, Elayna Wichert
Masters Theses & Specialist Projects
The objective of this study is to empirically test existing techniques to calculate the likely range of values for a Classical Test Theory true score given an observed score. The traditional method for forming these confidence intervals has used the standard error of measurement (SEM) as the basis for this confidence interval. An alternate equation, the standard error of estimate (SEE), has been recommended in place of the SEM for this purpose, yet it remains overlooked in the field of psychometrics. It is important that the correct equation be used in various applications in personnel psychology. Monte Carlo analyses were …
Psychometric Properties Of A Year-End Form Four Chemistry Paper 1 In Selected Schools In Petaling Utama District,
2020
Universiti Malaya
Psychometric Properties Of A Year-End Form Four Chemistry Paper 1 In Selected Schools In Petaling Utama District, Mohamed Razali Rose Enne Emellia
Student Works (2020-2029)
The low-performance of science stream students in Chemistry especially in high-stakes testing, has alarmed the stakeholders. This problem has led to the need of analyzing the instruments used to measure the achievement of students. The present study examined the psychometric properties of multiple-choice questions in the Year-end Form Four Chemistry Paper 1 using the Rasch Model in the Klang Valley. A quantitative research design has been used for this study. The sample comprised of 435 Form Four Pure Science students from four randomly selected secondary schools in the Petaling Utama district. The Rasch analysis was conducted in two stages. The …
Session 11 - Methods: Bootstrap Control Chart For Pareto Percentiles,
2020
University of South Dakota
Session 11 - Methods: Bootstrap Control Chart For Pareto Percentiles, Ruth Burkhalter
SDSU Data Science Symposium
Lifetime percentile is an important indicator of product reliability. However, the sampling distribution of a percentile estimator for any lifetime distribution is not a bell shaped one. As a result, the well-known Shewhart-type control chart cannot be applied to monitor the product lifetime percentiles. In this presentation, Bootstrap control charts based on maximum likelihood estimator (MLE) are proposed for monitoring Pareto percentiles. An intensive simulation study is conducted to compare the performance among the proposed MLE Bootstrap control chart and Shewhart-type control chart.
A Xgboost Risk Model Via Feature Selection And Bayesian Hyper-Parameter Optimization,
2020
Kennesaw State University
A Xgboost Risk Model Via Feature Selection And Bayesian Hyper-Parameter Optimization, Yan Wang, Sherry Ni
Published and Grey Literature from PhD Candidates
This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach for business risk classification. Feature selection (FS) algorithms and hyper-parameter optimizations are simultaneously considered during model training. The five most commonly used FS methods including weight by Gini, weight by Chi-square, hierarchical variable clustering, weight by correlation, and weight by information are applied to alleviate the effect of redundant features. Two hyper-parameter optimization approaches, random search (RS) and Bayesian tree-structuredParzen Estimator (TPE), are applied in XGBoost. The effect of different FS and hyper-parameter optimization methods on the model performance are investigated by the Wilcoxon Signed Rank …
An Automatic Interaction Detection Hybrid Model For Bankcard Response Classification,
2020
Kennesaw State University
An Automatic Interaction Detection Hybrid Model For Bankcard Response Classification, Yan Wang, Sherry Ni, Brian Stone
Published and Grey Literature from PhD Candidates
Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree-based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possible potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model …
A Two-Stage Hybrid Model By Using Artificial Neural Networks As Feature Construction Algorithms,
2020
Kennesaw State University
A Two-Stage Hybrid Model By Using Artificial Neural Networks As Feature Construction Algorithms, Yan Wang, Sherry Ni, Brian Stone
Published and Grey Literature from PhD Candidates
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage. The model is compared with the traditional one-stage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under the ROC curve, and KS statistic. By creating new …
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 …
Comprehensive Research Synthesis: An Approach To Mixed Methods Research Syntheses,
2020
University of Denver
Comprehensive Research Synthesis: An Approach To Mixed Methods Research Syntheses, Lilian Linialy Chimuma
Electronic Theses and Dissertations
Mixed methods research synthesis (MMRS) is an emerging application of both mixed-methods research (MMR) and review research. MMRS promises to comprehensively address intricate contemporary research and evaluation questions given diverse evidence sources (across quantitative, qualitative, and MMR primary studies). The significance of concurrently addressing methodological issues for new research developments is widely noted in the literature. Current efforts attempt to streamline methodological practices along with application of the MMRS approach. Researchers have proposed conceptual frameworks to guide the application and practice of MMRS studies. Despite these efforts, complications and disagreements persist. In response to these concerns, this study developed a …
