Biasing Estimator To Mitigate Multicollinearity In Linear Regression Model, 2023 Department of Mathematics and Statistics, Federal University Wukari, Wukari, Nigeria

#### 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.

Informative Hypothesis For Group Means Comparison, 2023 National University of Singapore

#### 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 …

Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, 2022 Nova Southeastern University

#### Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, Mikayla L. Twiss

*All HCAS Student Capstones, Theses, and Dissertations*

Trait-based ecology characterizes individuals’ functional attributes to better understand and predict their interactions with other species and their environments. Utilizing morphological traits to describe functional groups has helped group species with similar ecological niches that are not necessarily taxonomically related. Within the deep-pelagic fishes, the Order Stomiiformes exhibits high morphological and species diversity, and many species undertake diel vertical migration (DVM). While the morphology and behavior of stomiiform fishes have been extensively studied and described through taxonomic assessments, the connection between their form and function regarding their DVM types, morphotypes, and daytime depth distributions is not well known. Here, three …

Learning Graphical Models Of Multivariate Functional Data With Applications To Neuroimaging, 2022 Clemson University

#### 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 …

Statistical Roles Of The G-Expectation Framework In Model Uncertainty: The Semi-G-Structure As A Stepping Stone, 2022 The University of Western Ontario

#### Statistical Roles Of The G-Expectation Framework In Model Uncertainty: The Semi-G-Structure As A Stepping Stone, Yifan Li

*Electronic Thesis and Dissertation Repository*

The G-expectation framework is a generalization of the classical probability system based on the sublinear expectation to deal with phenomena that cannot be described by a single probabilistic model. These phenomena are closely related to the long-existing concern about model uncertainty in statistics. However, the distributions and independence in the G-framework are quite different from the classical setup. These distinctions bring difficulty when applying the idea of this framework to general statistical practice. Therefore, a fundamental and unavoidable problem is how to better understand G-version concepts from a statistical perspective.

To explore this problem, this thesis establishes a new substructure …

Regression-Based Methods For Dynamic Treatment Regimes With Mismeasured Covariates Or Misclassified Response, 2022 The University of Western Ontario

#### 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 …

Copulas, Maximal Dependence, And Anomaly Detection In Bi-Variate Time Series, 2022 The University of Western Ontario

#### Copulas, Maximal Dependence, And Anomaly Detection In Bi-Variate Time Series, Ning Sun

*Electronic Thesis and Dissertation Repository*

This thesis focuses on discussing non-parametric estimators and their asymptotic behaviors for indices developed to characterize bi-variate time series. There are typically two types of indices depending on whether the distributional information is involved. For the indices containing the distributional information of the bivariate stationary time series, we particularly focus on the index called the tail order of maximal dependence (TOMD), which is an improvement of the tail order. For the indices without distributional information of the bivariate time series, we focus on an anomaly detection index for univariate input-output systems.

This thesis integrates three articles. The first article (Chapter …

Bias-Corrected Bagging In Active Learning With An Actuarial Application, 2022 Western University

#### Bias-Corrected Bagging In Active Learning With An Actuarial Application, Yangxuan Xu

*Undergraduate Student Research Internships Conference*

The variable annuity (VA) is a modern insurance product that offers certain guaranteed protection and tax-deferred treatment. Because of the inherent complexity of guarantees’ payoff, the closed-form solution of fair market values (FMVs) is often not available. Most insurance companies depend on Monte Carlo (MC) simulation to price the FMVs of these products, which is an extremely computational intensive and time-consuming approach. The metamodeling approach can be used to circumvent the heavy computation.

In the modeling stage, the bagged tree method has proved to outperform other parametric approaches. Also, a bias-corrected (BC) bagging model was tried and showed significant improvement …

The Q-Analogue Of The Extended Generalized Gamma Distribution, 2022 Western University

#### The Q-Analogue Of The Extended Generalized Gamma Distribution, Wenhao Chen

*Undergraduate Student Research Internships Conference*

This project introduces a flexible univariate probability model referred to as the q-analogue of the Extended Generalized Gamma (or q-EGG) distribution, which encompasses the majority of the most frequently used continuous distributions, including the gamma, Weibull, logistic, type-1 and type-2 beta, Gaussian, Cauchy, Student-t and F. Closed form representations of its moments and cumulative distribution function are provided. Additionally, computational techniques are proposed for determining estimates of its parameters. Both the method of moments and the maximum likelihood approach are utilized. The effect of each parameter is also graphically illustrated. Certain data sets are modeled with q-EGG distributions; goodness of …

Practical T-Test Power Analysis With R, 2022 National University of Singapore

#### Practical T-Test Power Analysis With R, Teck Kiang Tan

*Practical Assessment, Research, and Evaluation*

Power analysis based on the analytical t-test is an important aspect of a research study to determine the sample size required to detect the effect for the comparison of two means. The current paper presents a reader-friendly procedure for carrying out the t-test power analysis using the various R add-on packages. While there is a growing of R users in the academic that uses R as the base for carrying out research, there is a lack of reference that discusses both frequentist and Bayesian approaches and point out their distinct features for t-test power analysis. The practical aspects of the …

Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, 2022 Southern Methodist University

#### Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun

*Statistical Science Theses and Dissertations*

Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of …

To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, 2022 University of Windsor

#### To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat

*Major Papers*

In this paper, we recommend a mechanism for determining whether to logit or not to logit data in the unit interval which is based on quantile estimation of data between 0 and 1. By using a simulated dataset generated from a Beta regression model, the estimated quantile for this model perform better than those based on the linear quantile regression with logit transformation.

Further, we investigate the performance of the quantile regression estimators based on the LQR and we conclude that it is better than those based on the Beta regression when the distribution is contaminated with 10% uniform numbers …

Advanced High Dimensional Regression Techniques, 2022 Clemson University

#### Advanced High Dimensional Regression Techniques, Yuan Yang

*All Dissertations*

This dissertation focuses on developing high dimensional regression techniques to analyze large scale data using both Bayesian and frequentist approaches, motivated by data sets from various disciplines, such as public health and genetics. More specifically, Chapters 2 and Chapter 4 take a Bayesian approach to achieve modeling and parameter estimation simultaneously while Chapter 3 takes a frequentist approach. The main aspects of these techniques are that they perform variable selection and parameter estimation simultaneously, while also being easily adaptable to large-scale data. In particular, by embedding a logistic model into traditional spike and slab framework and selecting of proper prior …

New Developments On The Estimability And The Estimation Of Phase-Type Actuarial Models, 2022 The University of Western Ontario

#### New Developments On The Estimability And The Estimation Of Phase-Type Actuarial Models, Cong Nie

*Electronic Thesis and Dissertation Repository*

This thesis studies the estimability and the estimation methods for two models based on Markov processes: the phase-type aging model (PTAM), which models the human aging process, and the discrete multivariate phase-type model (DMPTM), which can be used to model multivariate insurance claim processes.

The principal contributions of this thesis can be categorized into two areas. First, an objective measure of estimability is proposed to quantify estimability in the context of statistical models. Existing methods for assessing estimability require the subjective specification of thresholds, which potentially limits their usefulness. Unlike these methods, the proposed measure of estimability is objective. In …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, 2022 Wayne State University

#### The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

*Medical Student Research Symposium*

**Background: **Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

**Methods**: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_{2.5}), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Adjusting Community Survey Data Benchmarks For External Factors, 2022 Southern Methodist University

#### Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu

*SMU Data Science Review*

**Abstract.** Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest, ridge, decision …

A Study On Privacy Of Iot Devices Among A Sample Of Indians In The U.S- 2021, 2022 CHRIST

#### A Study On Privacy Of Iot Devices Among A Sample Of Indians In The U.S- 2021, Sahana Prasad Dr, Sharanya Prasad Ms, Vijith Raghavendra, Srishma Sunku

*International Journal of Computer Science and Informatics*

The Internet of Things (IoT) has gained immense popularity over the last decade with wide-ranging applications in domains of medicine, science, military as well as domestic use. Despite its tremendous growth, privacy concerns plague IoT applications and have the potential to hamper the benefits derived from its usage. This paper carries out a statistical analysis of empirical data collected from users of IoT to assess the level of awareness among users of IoT. The mode of study was through a questionnaire sent through Google forms to a selection of Indians living across the U.S. The place was chosen as some …

Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas, 2022 University of Arkansas, Fayetteville

#### Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas, Tayden Barretto

*Industrial Engineering Undergraduate Honors Theses*

The outbreak of COVID-19 has created a major crisis across the world since its start in 2019, and its influence on every realm of society is undeniable. Globally, more than 500 million cases have been recorded since March 2020, with almost 6 million deaths. In the wake of this crisis, many governments and health organizations have taken steps and precautions to mitigate its spread. These steps involve public mandates of information, reducing frequency of personal contact, and use of masks to minimize the risk of transmission. Current access to mobility data released from Google detailing population movements has provided a …

Attempting To Predict The Unpredictable: March Madness, 2022 University of Nebraska at Omaha

#### Attempting To Predict The Unpredictable: March Madness, Coleton Kanzmeier

*Theses/Capstones/Creative Projects*

Each year, millions upon millions of individuals fill out at least one if not hundreds of March Madness brackets. People test their luck every year, whether for fun, with friends or family, or to even win some money. Some people rely on their basketball knowledge whereas others know it is called March Madness for a reason and take a shot in the dark. Others have even tried using statistics to give them an edge. I intend to follow a similar approach, using statistics to my advantage. The end goal is to predict this year’s, 2022, March Madness bracket. To achieve …

Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, 2022 University of Arkansas, Fayetteville

#### Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, Jennifer A. Reimers

*Graduate Theses and Dissertations*

Researchers have recognized that respondents may not answer items in a way that accurately reflects their attitude or trait level being measured. The resulting response data that deviates from what would be expected has been shown to have significant effects on the psychometric properties of a scale and analytical results. However, many studies that have investigated the detection of aberrant data and its effects have done so using dominance item response theory (IRT) models. It is unknown whether the impacts of aberrant data and the methodology used to identify aberrant responding when using dominance IRT models apply similarly when scales …