Multivariate Analysis Commons^™

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, 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) …

The Expanded View Of Individualism And Collectivism: One, Two, Or Four Dimensions?, Jennifer L. Priestley, Kamal Fatehi, Gita Taasoobshirazi

Faculty Articles

Recent research to analyze and discuss cultural differences has employed a combination of five major dimensions of individualism–collectivism, power distance, uncertainty avoidance, femininity– masculinity (gender role differentiation), and long-term orientation. Among these dimensions, individualism–collectivism has received the most attention. Chronologically, this cultural attribute has been regarded as one, then two, and more recently, four dimensions of horizontal and vertical individualism and collectivism. However, research on this issue has not been conclusive and some have argued against this expansion. The current study attempts to explain and clarify this discussion by using a shortened version of the scale developed by Singelis et …

Interdependence Across Foreign Exchange Rate Markets- A Mixed Copula Approach, Richard Adjei-Boateng

Masters Theses & Specialist Projects

The purpose of this thesis is to study the dependence structure of exchange rate pairs using a mixture of copula as opposed to a single copula approach. Mixed copula models have the ability to generate dependence structures that do not belong to existing copula families. The flexibility in choosing component copulas in this mixture model aids the construction of a system that is simultaneously parsimonious and flexible enough to generate most dependence patterns in exchange rate data. Furthermore, the method of mixture copulas facilitates the separation of both the structure and degree of dependence, concepts that are respectively embodied in …

Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun

Theses and Dissertations

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in …

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 …

Theory Of Principal Components For Applications In Exploratory Crime Analysis And Clustering, Daniel Silva

All Graduate Theses, Dissertations, and Other Capstone Projects

The purpose of this paper is to develop the theory of principal components analysis succinctly from the fundamentals of matrix algebra and multivariate statistics. Principal components analysis is sometimes used as a descriptive technique to explain the variance-covariance or correlation structure of a dataset. However, most often, it is used as a dimensionality reduction technique to visualize a high dimensional dataset in a lower dimensional space. Principal components analysis accomplishes this by using the first few principal components, provided that they account for a substantial proportion of variation in the original dataset. In the same way, the first few principal …

Joint Simulation Of Continuous And Categorical Variables For Mineral Resource Modeling And Recoverable Reserves Calculation, Sentle Augustinus Hlajoane

Dissertations, Master's Theses and Master's Reports

Spatial variability and uncertainty of continuous variables (grade) and categorical variables (rock-types) in mineral evaluation significantly impact the economics of mining projects. The conventional approach of simulating grades using deterministic rock- types is problematic since spatial variability, and uncertainty of grades at rock-type contacts are not well captured in deposits where the grade changes gradually between rock-types. Therefore, jointly simulating these variables can improve confidence (reduce uncertainty) in a resource model. Also, resource classification and recoverable reserve calculation can significantly improve the understanding of the deposit and its economic viability. This research utilized the Plural-Gaussian geostatistical simulation to jointly simulate …

Zero-Inflated Longitudinal Mixture Model For Stochastic Radiographic Lung Compositional Change Following Radiotherapy Of Lung Cancer, Viviana A. Rodríguez Romero

Theses and Dissertations

Compositional data (CD) is mostly analyzed as relative data, using ratios of components, and log-ratio transformations to be able to use known multivariable statistical methods. Therefore, CD where some components equal zero represent a problem. Furthermore, when the data is measured longitudinally, observations are spatially related and appear to come from a mixture population, the analysis becomes highly complex. For this matter, a two-part model was proposed to deal with structural zeros in longitudinal CD using a mixed-effects model. Furthermore, the model has been extended to the case where the non-zero components of the vector might a two component mixture …

An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne

Master's Theses or Doctor of Nursing Practice

Convergence is an evolutionary phenomenon wherein distantly related organisms independently develop features or functional adaptations to overcome similar environmental constraints. Historically, convergence among organisms has been speculated or asserted with little rigorous or quantitative investigation. More recent advancements in systematics has allowed for the detection and study of convergence in a phylogenetic context, but this does little to elucidate convergent anatomical features in extinct taxa with poorly understood evolutionary histories. The purpose of this study is to investigate one potentially convergent system—the feeding structure of Xiphactinus audax (Teleostei: Ichthyodectiformes) and Megalops atlanticus (Teleostei: Elopiformes)—using a comparative anatomical approach to assess …

Nonparametric Analysis Of Clustered And Multivariate Data, Yue Cui

Theses and Dissertations--Statistics

In this dissertation, we investigate three distinct but interrelated problems for nonparametric analysis of clustered data and multivariate data in pre-post factorial design.

In the first project, we propose a nonparametric approach for one-sample clustered data in pre-post intervention design. In particular, we consider the situation where for some clusters all members are only observed at either pre or post intervention but not both. This type of clustered data is referred to us as partially complete clustered data. Unlike most of its parametric counterparts, we do not assume specific models for data distributions, intra-cluster dependence structure or variability, in effect …

Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu

Theses and Dissertations--Statistics

A common problem in regression analysis (linear or nonlinear) is assessing the lack-of-fit. Existing methods make parametric or semi-parametric assumptions to model the conditional mean or covariance matrices. In this dissertation, we propose fully nonparametric methods that make only additive error assumptions. Our nonparametric approach relies on ideas from nonparametric smoothing to reduce the test of association (lack-of-fit) problem into a nonparametric multivariate analysis of variance. A major problem that arises in this approach is that the key assumptions of independence and constant covariance matrix among the groups will be violated. As a result, the standard asymptotic theory is not …

Moment Kernels For T-Central Subspace, Weihang Ren

Theses and Dissertations--Statistics

The T-central subspace allows one to perform sufficient dimension reduction for any statistical functional of interest. We propose a general estimator using a third moment kernel to estimate the T-central subspace. In particular, in this dissertation we develop sufficient dimension reduction methods for the central mean subspace via the regression mean function and central subspace via Fourier transform, central quantile subspace via quantile estimator and central expectile subsapce via expectile estima- tor. Theoretical results are established and simulation studies show the advantages of our proposed methods.

Accounting For The Uncertainty Due To Chemicals Below The Detection Limit In Mixture Analysis, Paul M. Hargarten

Theses and Dissertations

Humans are exposed to multiple chemicals every day. Epidemiological studies have shown that chemical mixtures are associated with cancers, allergies, neurodevelopmental disorders, and other adverse health effects. To assess these associations, investigators are increasingly using chemical mixture approaches like weighted quantile sum (WQS) regression. In these studies, the research objectives are to determine whether a mixture of correlated chemicals is associated with an adverse health outcome and to identify the important chemicals. However, as experimental equipment measures each exposure to a chemical-specific detection limit, the exposures are unknown between zero and the detection limit. Indeed, the number of exposures below …

Inventory Models For Perishable Items Under Markdown Policy, Nurzahara Atika Kamaruzaman

Student Works (2020-2029)

As expected, the demand for a fresh product depends on how fresh it is, therefore, it is important to take expiration date into consideration. Based on marketing and economic theory, several factors such as price, inventory level and advertisement play a crucial role in influencing the demand. Hence, we study the effect of these factors in influencing the demand in the inventory model. Since the demand for perishable product declines over time, markdown policy is offered to increase the demand and profit while reducing the inventory. Salvage value is incorporated to the deteriorating units. In this research, we extend previous …

Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, Oluwasegun O. Abatan

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fluvial deposits represent some of the best hydrocarbon reservoirs, but the quality of fluvial reservoirs varies depending on the reservoir architecture, which is controlled by allogenic and autogenic processes. Allogenic controls, including paleoclimate, tectonics, and glacio-eustasy, have long been debated as dominant controls in the deposition of fluvial strata. However, recent research has questioned the validity of this cyclicity and may indicate major influence from autogenic controls. To further investigate allogenic controls on stratal order, I analyzed the facies architecture, geomorphology, paleohydrology, and the stratigraphic framework of the Middle Pennsylvanian Allegheny Formation (MPAF), a fluvial depositional system in the Appalachian …

Three Essays On Health Economics And Policy Evaluation, Shishir Shakya

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation consists of three essays on the U.S. Health care policy. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively. I provide quantitative evidence on how much Prescription Drug Monitoring Programs (PDMPs) affects the retail opioid prescribing behaviors. Using the American Community Survey (ACS), I retrieve county-level high dimensional panel data set from 2010 to 2017. I employ three separate identification strategies: difference-in-difference, double selection post-LASSO, and spatial difference-in-difference. I compare how the retail opioid prescribing behaviors of counties, that are mandatory for prescribers to check the PDMP before prescribing controlled substances …

Generalized Matrix Decomposition Regression: Estimation And Inference For Two-Way Structured Data, Yue Wang, Ali Shojaie, Tim Randolph, Jing Ma

UW Biostatistics Working Paper Series

Analysis of two-way structured data, i.e., data with structures among both variables and samples, is becoming increasingly common in ecology, biology and neuro-science. Classical dimension-reduction tools, such as the singular value decomposition (SVD), may perform poorly for two-way structured data. The generalized matrix decomposition (GMD, Allen et al., 2014) extends the SVD to two-way structured data and thus constructs singular vectors that account for both structures. While the GMD is a useful dimension-reduction tool for exploratory analysis of two-way structured data, it is unsupervised and cannot be used to assess the association between such data and an outcome of interest. …

Statistical Inference For Networks Of High-Dimensional Point Processes, Xu Wang, Mladen Kolar, Ali Shojaie

UW Biostatistics Working Paper Series

Fueled in part by recent applications in neuroscience, high-dimensional Hawkes process have become a popular tool for modeling the network of interactions among multivariate point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work have only focused on estimation. To bridge this gap, this paper proposes a high-dimensional statistical inference procedure with theoretical guarantees for multivariate Hawkes process. Key to this inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarizes the entire history of the process. We apply this …

Function Space Tensor Decomposition And Its Application In Sports Analytics, Justin Reising

Electronic Theses and Dissertations

Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this …