Statistical Models Commons^™

Utilizing Markov Chains To Estimate Allele Progression Through Generations, Ronit Gandhi

Honors Theses

All populations display patterns in allele frequencies over time. Some alleles cease to exist, while some grow to become the norm. These frequencies can shift or stay constant based on the conditions the population lives in. If in Hardy-Weinberg equilibrium, the allele frequencies stay constant. Most populations, however, have bias from environmental factors, sexual preferences, other organisms, etc. We propose a stochastic Markov chain model to study allele progression across generations. In such a model, the allele frequencies in the next generation depend only on the frequencies in the current one.

We use this model to track a recessive allele …

Application Of A Hybrid Statistical–Dynamical System To Seasonal Prediction Of North American Temperature And Precipitation, Sarah Strazzo, Dan C. Collins, Andrew Schepen, Q. J. Wang, Emily Becker, Liweli Jia

Publications

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration …

The Influence Of Model Resolution On The Simulated Sensitivity Of North Atlantic Tropical Cyclone Maximum Intensity To Sea Surface Temperature, Sarah Strazzo, James Elsner, Timothy Larow, Hiroyuki Murakami, Michael Wehner, Ming Zhao

Publications

No abstract provided.

Binomial Regression With A Misclassified Covariate And Outcome., Sheng Luo, Wenyaw Chan, Michelle A Detry, Paul J Massman, R S. Doody

Faculty Publications

Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease-exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach …

Quantifying The Sensitivity Of Maximum, Limiting, And Potential Tropical Cyclone Intensity To Sst: Observations Versus The Fsu/ Coaps Global Climate Model, Sarah Strazzo, James Elsner, Tim Larow

Publications

No abstract provided.

Hidden Trends In Nfl Data, Scott Santor

Statistics

This is an analysis on National Football League (NFL) data for the 2013-2014 regular season. The main goal is to find hidden trends in game data that can ultimately determine which factors are statistically significant to award a team with their ultimate objective, a win.

The main response variable to be examined is total wins throughout the regular season, and an alternative dependent variable is spread; the difference between a team’s points scored, and points against. Spread is analyzed to provide a different quantitative response variable that can be both positive and negative.

Game data was gathered from ESPN.com box …

Sensitivity Of Limiting Hurricane Intensity To Sst In The Atlantic From Observations And Gcms, James Elsner, Sarah Strazzo, Thomas H. Jagger, Timothy Larow, Ming Zhao

Publications

No abstract provided.

Water Quality Models For Stormwater Runoff In Two Lincoln, Nebraska Urban Watersheds, Jake Fisher

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Water quality monitoring was conducted in two urban watersheds (Colonial Hills and Taylor Park) located in southeast Lincoln, NE over a three year period spanning from October 2008 through September 2011. In-line probes continuously measured for turbidity, conductivity, dissolved oxygen, and water temperature while other water quality constituents were analyzed for discrete water samples collected using grab and automatic sampling techniques. The water quality data was used to calculate event mean concentrations (EMCs) for sixteen storm events sampled over the duration of the project period. Three types of stormwater quality multiple linear regression models were developed for the estimation of …

The Location Decisions Of Foreign Investors In China: Untangling The Effect Of Wages Using A Control Function Approach, Xuepeng Liu, Mary E. Lovely, Jan Ondrich

Faculty and Research Publications

There is almost no support for the proposition that capital is attracted to low wages from firm-level studies. We examine the location choices of 2,884 firms investing in China between 1993 and 1996 to offer two main contributions. First, we find that the location of labor-intensive activities is highly elastic to provincial wage differences. Generally, investors' wage sensitivity declines as the skill intensity of the industry increases. Second, we find that unobserved location-specific attributes exert a downward bias on estimated wage sensitivity. Using a control function approach, we estimate a downward bias of 50% to 90% in wage coefficients estimated …

Mechanistic Home Range Models And Resource Selection Analysis: A Reconciliation And Unification, Paul R. Moorcroft, Alex Barnett

Dartmouth Scholarship

In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this …

Marginal Modeling Of Multilevel Binary Data With Time-Varying Covariates, Diana Miglioretti, Patrick Heagerty

UW Biostatistics Working Paper Series

We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors; and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.