Statistical Models Commons^™

Biasing Estimator To Mitigate Multicollinearity In Linear Regression Model, Abdulrasheed Bello Badawaire, Issam Dawoud, Adewale Folaranmi Lukman, Victoria Laoye, Arowolo Olatunji

Al-Bahir Journal for Engineering and Pure Sciences

A new two-parameter estimator was developed to combat the threat of multicollinearity for the linear regression model. Some necessary and sufficient conditions for the dominance of the proposed estimator over ordinary least squares (OLS) estimator, ridge regression estimator, Liu estimator, KL estimator, and some two-parameter estimators are obtained in the matrix mean square error sense. Theory and simulation results show that, under some conditions, the proposed two-parameter estimator consistently dominates other estimators considered in this study. The real-life application result follows suit.

On Partially Observed Tensor Regression, Dinara Miftyakhetdinova

Major Papers

Tensor data is widely used in modern data science. The interest lies in identifying and characterizing the relationship between tensor datasets and external covariates. These datasets, though, are often incomplete. An efficient nonconvex alternating updating algorithm proposed by J. Zhou et al. in the paper "Partially Observed Dynamic Tensor Response Regression" provides a novel approach. The algorithm handles the problem of unobserved entries by solving an optimization problem of a loss function under the low-rankness, sparsity, and fusion constraints. This analysis aims to understand in detail the proposed algorithms and their theoretical proofs with, potentially, dropping some of the assumptions …

Informative Hypothesis For Group Means Comparison, Dr. Teck Kiang Tan

Practical Assessment, Research, and Evaluation

Researchers often have hypotheses concerning the state of affairs in the population from which they sampled their data to compare group means. The classical frequentist approach provides one way of carrying out hypothesis testing using ANOVA to state the null hypothesis that there is no difference in the means and proceed with multiple comparisons if the null hypothesis is rejected. As this approach is not able to incorporate order, inequality, and direction into hypothesis testing, and neither does it able to specify multiple hypotheses, this paper introduces the informative hypothesis that allows more flexibility in stating hypothesis testing and is …

Uniformity Test Based On The Empirical Bernstein Distribution, Ran Sun

Major Papers

In this paper, we firstly review the origin of Bernstein polynomial and the various application of it. Then we review the importance of goodness-of-fit test, especially the uniformity test, and we examine lots of different test statistics proposed by far. After that we suggest two new statistics for testing the uniformity. These two statistics are based on Komogorov-Smirnov test type and Cramér-Von Mises test type, respectively. Also we embed Bernstein polynomial into those test type and take advantage of great approximation performance of this polynomial. Finally, we run a Monte-Carlo simulation to compare the performance of our statistics to those …

Application Of Sentiment Analysis And Machine Learning Techniques To Predict Daily Cryptocurrency Price Returns, Edward Wu

CMC Senior Theses

This paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price …

Statistical Models For Decision-Making In Professional Soccer, Sean Hellingman

Theses and Dissertations (Comprehensive)

As soccer is widely regarded as the most popular sport in the world there is high interest in methods of improving team performances. There are many ways teams and individual athletes can influence their own performances during competition. This thesis focuses on developing statistical methodologies for improving competition-based decision-making for soccer so as to allow professional soccer teams to make better informed decisions regarding player selection and in-game decision-making.

To properly capture the dynamic actions of professional soccer, Markov chains with increasing complexity are proposed. These models allow for the inclusion of potential changes in the process caused by goals …

Study On Innovation Networks And Its Spillover Effect Of China’S New Energy Automobile Industry, Zhifei Xiong, Wenzhong Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The network spillover effect of knowledge has been playing an increasingly significant role in the development of industrial innovation. The urban cooperation matrix of China’s new energy automobile industry is built based on new energy automobile patent data, and the structure and evolution process of China’s new energy automobile industry are depicted. On this basis, the spatial Dubin model (SDM) is used to calculate the network spillover effect, and its results are compared with the results of spillover effect based on the relationship of spatial contiguity and distance of cities. The results show that the innovation activities of China’s new …

Learning Graphical Models Of Multivariate Functional Data With Applications To Neuroimaging, Jiajing Niu

All Dissertations

This dissertation investigates the functional graphical models that infer the functional connectivity based on neuroimaging data, which is noisy, high dimensional and has limited samples. The dissertation provides two recipes to infer the functional graphical model: 1) a fully Bayesian framework 2) an end-to-end deep model.

We first propose a fully Bayesian regularization scheme to estimate functional graphical models. We consider a direct Bayesian analog of the functional graphical lasso proposed by Qiao et al. (2019).. We then propose a regularization strategy via the graphical horseshoe. We compare both Bayesian approaches to the frequentist functional graphical lasso, and compare the …

Evaluation Of Circular Logistic Regression Models With Asymmetrical Link Functions, Feridun Tasdan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Estimating R0 For Dengue Emergence In Central Argentina Using Statistical Models, Sahil Chindal

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bayesian Estimation Of The Intensity Function Of A Non-Homogeneous Poisson Process, James Jensen

Theses

In this paper we explore Bayesian inference and its application to the problem of estimating the intensity function of a non-homogeneous Poisson process. These processes model the behavior of phenomena in which one or more events, known as arrivals, occur independently of one another over a certain period of time. We are concerned with the number of events occurring during particular time intervals across several realizations of the process. We show that given sufficient data, we are able to construct a piecewise-constant function which accurately estimates the mean rates on particular intervals. Further, we show that as we reduce these …

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

Regression-Based Methods For Dynamic Treatment Regimes With Mismeasured Covariates Or Misclassified Response, Dan Liu

Electronic Thesis and Dissertation Repository

The statistical study of dynamic treatment regimes (DTRs) focuses on estimating sequential treatment decision rules tailored to patient-level information across multiple stages of intervention. Regression-based methods in DTR have been studied in the literature with a critical assumption that all the observed variables are precisely measured. However, this assumption is often violated in many applications. One example is the STAR*D study, in which the patient's depressive score is subject to measurement error. In this thesis, we explore problems in the context of DTR with measurement error or misclassification considered in the observed data.

The first project deals with covariate measurement …

Between “Breaking” And “Building”: The Bridge Theory Of Research Evaluation, Fang Xu, Xiaoxuan Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

How to build "new standards" after breaking "Siwei" is a hot and difficult issue in the current reform of research evaluation, which urgently needs good theoretical and methodological support. In this context, this study puts forward the BRIDGE theory of research evaluation of scientific researchers' achievements, which is to integrate the reasonable elements in the quantitative evaluation based on SCI papers into the "new standard" based on peer review, so as to build a bridge between quantitative analysis and qualitative evaluation. The practical application of BRIDGE theory is expressed as "Six Steps", in which the second step "Recode" and the …

Exploring Human-Caused Fire Occurrence Prediction, Ruyi Jin

Undergraduate Student Research Internships Conference

Wildland Fire Science has become an increasingly hot topic in recent years. The goal of this report is to investigate human-caused wildland fire occurrence prediction. The two main predictors of interest are the mean value of the Fine Fuel Moisture Code (FFMC) and the month when a fire ignites. An Exploratory Data Analysis is presented first, after which we fit models to predict daily fire counts. We first consider Poisson models to fit the count data, but also attempt to fit Negative Binomial models to deal with overdispersion. We compare these models in the following ways: plotting the difference in …

An Analysis Of Weighted Least Squares Monte Carlo, Xiaotian Zhu

Electronic Thesis and Dissertation Repository

Since Longstaff and Schwartz [2001] brought the amazing Regression-based Monte Carlo (LSMC) method in pricing American options, it has received heated discussion. Based on the research done by Fabozzi et al. [2017] that applies the heteroscedasticity correction method to LSMC, we further extend the study by introducing the methods from Park [1966] and Harvey [1976]. Our work shows that for a single stock American Call option modelled by GBM with two exercise opportunities, WLSMC or IRLSMC provides better estimates in continuation value than LSMC. However, they do not lead to better exercise decisions and hence have little to no effect …

A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming

Undergraduate Student Research Internships Conference

Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning …