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Articles 1 - 12 of 12
Full-Text Articles in Statistical Methodology
Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako
Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako
Doctoral Dissertations
This dissertation is in the field of Nonparametric Derivative Estimation using
Penalized Splines. It is conducted in two parts. In the first part, we study the L2
convergence rates of estimating derivatives of mean regression functions using penalized splines. In 1982, Stone provided the optimal rates of convergence for estimating derivatives of mean regression functions using nonparametric methods. Using these rates, Zhou et. al. in their 2000 paper showed that the MSE of derivative estimators based on regression splines approach zero at the optimal rate of convergence. Also, in 2019, Xiao showed that, under some general conditions, penalized spline estimators …
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Doctoral Dissertations
Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal …
Using Generalizability And Rasch Measurement Theory To Ensure Rigorous Measurement In An International Development Education Evaluation, Louise Bahry
Doctoral Dissertations
Between the United States and Great Britain, over 30 billion USD was spent in 2018 on international aid, over a billion of which is dedicated to education programs alone. Recently, there has been increased attention on the rigorous evaluation of aid-funded programs, moving beyond counting outputs to the measurement of educational impact. The current study uses two methodological approaches (Generalizability (Brennan, 1992, 2001) and Rasch Measurement Theory (Andrich, 1978; Rasch, 1980; Wright & Masters, 1982) to analyze data from math and literacy assessments, and self-report surveys used in an international evaluation of an educational initiative in the Democratic Republic of …
Model-Free Descriptive Modeling For Multivariate Categorical Data With An Ordinal Dependent Variable, Li Wang
Model-Free Descriptive Modeling For Multivariate Categorical Data With An Ordinal Dependent Variable, Li Wang
Doctoral Dissertations
In the process of statistical modeling, the descriptive modeling plays an essential role in accelerating the formulation of plausible hypotheses in the subsequent explanatory modeling and facilitating the selection of potential variables in the subsequent predictive modeling. Especially, for multivariate categorical data analysis, it is desirable to use the descriptive modeling methods for uncovering and summarizing the potential association structure among multiple categorical variables in a compact manner. However, many classical methods in this case either rely on strong assumptions for parametric models or become infeasible when the data dimension is higher. To this end, we propose a model-free method …
Latent Class Models For At-Risk Populations, Shuaimin Kang
Latent Class Models For At-Risk Populations, Shuaimin Kang
Doctoral Dissertations
Clustering Network Tree Data From Respondent-Driven Sampling With Application to Opioid Users in New York City There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete. Respondent-driven sampling (RDS) is a widely used method for sampling hard-to-reach human populations based on tracing links in the underlying unobserved social network. The resulting data therefore have tree structure representing a sub-sample of the network, along with many nodal attributes. In this paper, we introduce an approach to adjust mixture models …
Allocative Poisson Factorization For Computational Social Science, Aaron Schein
Allocative Poisson Factorization For Computational Social Science, Aaron Schein
Doctoral Dissertations
Social science data often comes in the form of high-dimensional discrete data such as categorical survey responses, social interaction records, or text. These data sets exhibit high degrees of sparsity, missingness, overdispersion, and burstiness, all of which present challenges to traditional statistical modeling techniques. The framework of Poisson factorization (PF) has emerged in recent years as a natural way to model high-dimensional discrete data sets. This framework assumes that each observed count in a data set is a Poisson random variable $y ~ Pois(\mu)$ whose rate parameter $\mu$ is a function of shared model parameters. This thesis examines a specific …
Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data, Caroline Kusiak
Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data, Caroline Kusiak
Masters Theses
Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely …
Statistical Methods On Risk Management Of Extreme Events, Zijing Zhang
Statistical Methods On Risk Management Of Extreme Events, Zijing Zhang
Doctoral Dissertations
The goal of the dissertation is the investigation of financial risk analysis methodologies, using the schemes for extreme value modeling as well as techniques from copula modeling. Extreme value theory is concerned with probabilistic and statistical questions re- lated to unusual behavior or rare events. The subject has a rich mathematical theory and also a long tradition of applications in a variety of areas. We are interested in its application in risk management, with a focus on estimating and forcasting the Value-at-Risk of financial time series data. Extremal data are inherently scarce, thus making inference challenging. In order to obtain …
Variable Selection In Single Index Varying Coefficient Models With Lasso, Peng Wang
Variable Selection In Single Index Varying Coefficient Models With Lasso, Peng Wang
Doctoral Dissertations
Single index varying coefficient model is a very attractive statistical model due to its ability to reduce dimensions and easy-of-interpretation. There are many theoretical studies and practical applications with it, but typically without features of variable selection, and no public software is available for solving it. Here we propose a new algorithm to fit the single index varying coefficient model, and to carry variable selection in the index part with LASSO. The core idea is a two-step scheme which alternates between estimating coefficient functions and selecting-and-estimating the single index. Both in simulation and in application to a Geoscience dataset, we …
Robust Optimization Of Biological Protocols, Patrick Flaherty, Ronald W. Davis
Robust Optimization Of Biological Protocols, Patrick Flaherty, Ronald W. Davis
Mathematics and Statistics Department Faculty Publication Series
When conducting high-throughput biological experiments, it is often necessary to develop a protocol that is both inexpensive and robust. Standard approaches are either not cost-effective or arrive at an optimized protocol that is sensitive to experimental variations. Here, we describe a novel approach that directly minimizes the cost of the protocol while ensuring the protocol is robust to experimental variation. Our approach uses a risk-averse conditional value-at-risk criterion in a robust parameter design framework. We demonstrate this approach on a polymerase chain reaction protocol and show that our improved protocol is less expensive than the standard protocol and more robust …
Determinants Of Health Care Use Among Rural, Low-Income Mothers And Children: A Simultaneous Systems Approach To Negative Binomial Regression Modeling, Swetha Valluri
Masters Theses 1911 - February 2014
The determinants of health care use among rural, low-income mothers and their children were assessed using a multi-state, longitudinal data set, Rural Families Speak. The results indicate that rural mothers’ decisions regarding health care utilization for themselves and for their child can be best modeled using a simultaneous systems approach to negative binomial regression. Mothers’ visits to a health care provider increased with higher self-assessed depression scores, increased number of child’s doctor visits, greater numbers of total children in the household, greater numbers of chronic conditions, need for prenatal or post-partum care, development of a new medical condition, and …
Dynamic Model Pooling Methodology For Improving Aberration Detection Algorithms, Brenton J. Sellati
Dynamic Model Pooling Methodology For Improving Aberration Detection Algorithms, Brenton J. Sellati
Masters Theses 1911 - February 2014
Syndromic surveillance is defined generally as the collection and statistical analysis of data which are believed to be leading indicators for the presence of deleterious activities developing within a system. Conceptually, syndromic surveillance can be applied to any discipline in which it is important to know when external influences manifest themselves in a system by forcing it to depart from its baseline. Comparing syndromic surveillance systems have led to mixed results, where models that dominate in one performance metric are often sorely deficient in another. This results in a zero-sum trade off where one performance metric must be afforded greater …