Statistics
Recent Submissions

Infinite dimensional Stochastic Inverse Problems
In many disciplines, mathematical models such as differential equations, are used to characterize physical systems. The model induces a complex nonlinear measurable map from the domain of physical parameters to the range ... 
Survey estimators of domain means under shape restrictions
Novel methodologies that introduce shaperestricted regression techniques into survey domain estimation and inference are presented in this dissertation. Although population domain means are frequently expected to respect ... 
Statistical models for dependent trajectories with application to animal movement
In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical models for dependent trajectories in which depedencies among ... 
Weighting adjustments in surveys
We consider three topics in this dissertation: 1) Nonresponse weighting adjustment using penalized spline regression; 2) Improving survey estimators through weight smoothing; and 3) An investigation of weight smoothing ... 
Nonparametric tests of spatial isotropy and a calibrationcapturerecapture model
In this dissertation we present applied, theoretical, and methodological advances in the statistical analysis of spatiallyreferenced and capturerecapture data. An important step in modeling spatially referenced data is ... 
Some topics on modelbased clustering
Cluster analysis is widely applied in various areas. Modelbased clustering, which assumes a mixture model, is one of the most useful approaches in clustering. Using modelbased clustering, we can make statistical inferences ... 
Estimation and linear prediction for regression, autoregression and ARMA with infinite variance data
This dissertation is divided into four parts, each of which considers random variables from distributions with regularly varying tails and/or in a stable domain of attraction. Part I considers the existence of infinite ... 
ChangePoint estimation using shaperestricted regression splines
ChangePoint estimation is in need in fields like climate change, signal processing, economics, doseresponse analysis etc, but it has not yet been fully discussed. We consider estimating a regression function ƒm and a ... 
Inference for functional time series with applications to yield curves and intraday cumulative returns
Econometric and financial data often take the form of a functional time series. Examples include yield curves, intraday price curves and term structure curves. Before an attempt is made to statistically model or predict ... 
Modeling the upper tail of the distribution of facial recognition nonmatch scores
In facial recognition applications, the upper tail of the distribution of nonmatch scores is of interest because existing algorithms classify a pair of images as a match if their score exceeds some high quantile of the ... 
Improved estimation and prediction for computationally expensive ecological and paleoclimate models
In this dissertation, we present statistical methods to evaluate estimation and prediction performance for applied ecological problems. We explore a variety of applied problems and, within this context, we investigate how ... 
Statistical innovations for estimating shape characteristics of biological macromolecules in solution using smallangle xray scattering data
Smallangle Xray scattering (SAXS) is a technique that yields lowresolution images of biological macromolecules by exposing a solution containing the molecule to a powerful Xray beam. The beam scatters when it interacts ... 
Penalized isotonic regression and an application in survey sampling
In isotonic regression, the mean function is assumed to be monotone increasing (or de creasing) but otherwise unspecified. The classical isotonic leastsquares estimator is known to be inconsistent at boundaries; this is ... 
Modulated renewal process models with functional predictors for neural connectivities
Recurrent event data arise in fields such as medicine, business and social sciences. In general, there are two types of recurrent event data. One is from a relatively large number of independent processes exhibiting a ... 
A penalized estimation procedure for varying coefficient models
Varying coefficient models are widely used for analyzing longitudinal data. Various methods for estimating coefficient functions have been developed over the years. We revisit the problem under the theme of functional ... 
Improved Estimation of the Radius of Gyration from SmallAngle XRay Scattering Data
Smallangle Xray scattering (SAXS) is an experimentally simple technique that provides access to lowresolution information about biological macromolecules in solution. We here provide R code and example data sets for a ... 
Adjusting for capture, recapture, and identity uncertainty when estimating detection probability from capturerecapture surveys
When applying capturerecapture analysis methods, estimates of detection probability, and hence abundance estimates, can be biased if individuals of a population are not correctly identified (Creel et. al., 2003). My ... 
Parameter inference and model selection for differential equation models
Firstly, we consider the problem of estimating parameters of stochastic differential equations with discretetime observations that are either completely or partially observed. The transition density between two observations ... 
Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling
This dissertation presents novel work in statistical methods for extremes. Our underlying modeling procedure identifies the linear combination of covariates that is associated with extreme values of a response variable, ... 
Analysis of Structured Data and Big Data with Application to Neuroscience
Neuroscience research leads to a remarkable set of statistical challenges, many of them due to the complexity of the brain, its intricate structure and dynamical, nonlinear, often nonstationary behavior. The challenge ...