Browsing by Author "John Monahan, Committee Member"

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    Biological Control, Host Resistance, and Vegetative Propagation: Strategies and Tools for Management of the Invasive Hemlock Woolly Adelgid, Adelges tsugae Annand.
    (2008-09-04) Jetton, Robert Miller; David Orr, Committee Member; John Frampton, Committee Member; John Monahan, Committee Member; Fred Hain, Committee Chair; Dan Robison, Committee Co-Chair
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    Evaluating the Rationality of The Wall Street Journal's Panel of Economists
    (2003-12-17) Houck, Adam Christopher; Douglas Pearce, Committee Chair; John Lapp, Committee Member; John Monahan, Committee Member
    This paper will explore a methodology that will examine the difference between average and individual forecasts, concentrating on whether individual Wall Street Journal forecasters are unbiased and efficient. This result is important because the past literature has examined the accuracy of average forecasts, not individuals. In addition, a brief evaluation of Lamont's (2002) hypothesis will follow. Lamont determined that as forecasters become older and more established, in many instances deviations from the consensus forecast grew with time. The method adopted will allow for the testing of whether individual forecasts are unbiased and rational, telling more about how individuals, not averages, behave in broader contexts.
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    A Hedonic, Hedonic Metric and Logistic Approach to Estimating Demand for Fluid Milk Products Using Micro Level Data
    (2008-11-10) Gulseven, Osman; John Monahan, Committee Member; Michael Wohlgenant, Committee Chair; Nick Piggott, Committee Member; Barry Goodwin, Committee Member; Raymond Palmquist, Committee Member
    This study analyzes the market for functionally enhanced milk products. The first paper investigates the factors that derive the demand for soymilk products based on a two-stage hedonic model. In the first stage, the relationship between the prices of dairy products and the attributes of these products are exploited to derive the marginal implicit attribute prices. In the second stage, we used these prices along with the information on households’ demographic background to explain the demand for product attributes. Our results indicate that although the soymilk taste is undesirable, since soymilk is lactose/cholesterol free (LFCF) and mostly organic, it has a higher price premium than other milk types. In the second paper, we introduced the concept of Hedonic Metric (HM) approach as an approximation method to estimate the price elasticities in classical traditional models. In these models, the number of estimated parameters increases exponentially with the number of variables included in the model. HM method applied in this paper is practical and significantly reduces the number of parameters. The HM approach is compared with the Distance Metric (DM) approach of Rojas and Peterson (2008) to see which method gives better approximations to original LA/AIDS and RM models. In the last paper, we applied a two-stage logistic estimation to analyze consumer attitudes towards specialty milk types. In the first stage of our estimation we estimated the hedonic attribute prices. In the second stage we used these implicit prices along with the demographic information on households to estimate the factors that affect households’ decision to purchase specialty milk types. Our results indicate that while CFLF attribute and soy attribute are complementary with each other, whereas the organic attribute is a substitute to them. Minority households have a much higher probability of purchasing specialty milk types than white households.
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    An Innovative Hedonic Pricing System for US Cotton Based on HVI Quality Measures and Market Factors
    (2008-01-08) Cho, Hyunjung; Pamela Banks-Lee, Committee Member; Michelle Jones, Committee Member; John Monahan, Committee Member; Moon W. Suh, Committee Chair
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    Model-Robust Interval Estimation
    (2003-04-29) Gotwalt, Christopher Michael; Leonard Stefanski, Committee Member; Charlie Smith, Committee Member; Dennis Boos, Committee Chair; John Monahan, Committee Member
    Confidence intervals are one of the most useful statistical tools. This dissertation is a study of several methods for forming confidence intervals that are insensitive to model assumptions, provided that the mean model for the data is not misspecified. The most commonly used robust confidence interval, the generalized Wald interval, is known to be liberal in small sample situations. We investigate several alternatives to the generalized Wald interval that are shown to often have superior performance: the generalized score interval; the robust profile likelihood interval; a new, modified generalized score interval that we call the GS2 interval; and we investigate a bootstrap calibration of the generalized Wald interval. We also introduce a new general procedure, length-optimal interval estimation, that takes an existing equal-tail confidence procedure and creates a new one whose length is shorter than the original. Surprisingly, in simulations we see that these shorter intervals are shown to sometimes enjoy higher coverage than their standard counterparts.
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    Predatory Mites (Acari: Phytoseiidae) in Fraser fir Christmas Tree Plantations in the Southern Appalachians of North Carolina
    (2008-07-07) Williams, Jay Logan; David Orr, Committee Member; John Frampton, Committee Member; John Monahan, Committee Member; Fred P. Hain, Committee Chair
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    Resin Flow in Clonal Loblolly Pine
    (2007-08-22) Blinka, Kate Whitley; John Monahan, Committee Member; Fred P. Hain, Committee Chair; Barry Goldfarb, Committee Member
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    Statistical Analysis in Two Stage Randomization Designs in Clinical Trials
    (2005-06-25) Guo, Xiang; Daowen Zhang, Committee Member; John Monahan, Committee Member; Marie Davidian, Committee Member; Anastasios A. Tsiatis, Committee Chair
    Two-stage randomization designs are becoming more common in many clinical trials related to diseases such as cancer and HIV, where an induction therapy is given followed by a maintenance therapy depending on patients' response and consent. The main interest is to compare combinations of induction and maintenance therapies and to find the combination leading to the longest average survival time. However, in practice, the data analysis is typically conducted separately in two stages. In this Thesis, we tackle the problem based on treatment policies. We use the concepts of counting process and risk set as described by Fleming and Harrington (1991) to find weighted estimating equations whose solution gives an estimator for the cumulative hazard function which, in turn, is used to derive an estimator for the overall survival distribution under a treatment policy with right-censored data. We call this estimator as the Weighted Risk Set Estimator (WRSE). We show that the WRSE is consistent and asymptotically normally distributed. In addition to survival distribution estimation, we also consider the hypothesis testing problem. Since the log rank test is the common method for hypothesis testing in survival analysis, we propose a test statistic using an inverse weighted version of the log rank test. We use simulation studies to demonstrate the properties of our method and use data from a clinical trial, Protocol 88923, conducted by the Cancer and Leukemia Group B (CALGB) to illustrate how to implement the method.
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    Topics in Application of Nonparametric Smoothing Splines
    (2005-12-14) Lin, Jiang; Marie Davidian, Committee Co-Chair; John Monahan, Committee Member; Hao (Helen) Zhang, Committee Member; Daowen Zhang, Committee Chair
    There are two topics in this dissertation. The first topic is 'Smoothing Parameter Selection in Nonparametric Generalized Linear Models via Sixth-order Laplace Approximation' and the second topic is 'Smoothing Spline-based Score Tests for Proportional Hazards Models'. We present a new approach for the automatic selection of the smoothing parameter in nonparametric smoothing spline Generalized Linear Models (GLMs), using the Restricted Maximum Likelihood (REML) method and the sixth-order Laplace approximation of Raudenbush et al. (2000). The proposed approach is compared with Generalized Additive Mixed Model (GAMM, Lin and Zhang 1999) and Generalized Approximate Cross-Validation (GACV, Gu and Xiang 2001) through simulations and is shown to be effective. We propose 'score-type' tests for the proportional hazards assumption and for covariate effects in the Cox model, using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood. By treating the inverse of the smoothing parameter as a variance component, we derive the score tests by testing an equivalent null hypothesis that the corresponding variance component is zero. The tests are shown to have size close to the nominal level and to provide good power against general alternatives in simulations. We apply the proposed tests to data from a cancer clinical trial.
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    Variable Selection in Semi-parametric Additive Models with Extensions to High Dimensional Data and Additive Cox Models
    (2008-06-27) Liu, Song; Hao Helen Zhang, Committee Chair; Dennis Boos, Committee Member; Wenbin Lu, Committee Member; John Monahan, Committee Member