Browsing by Subject "bootstrap"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Bootstrap Techniques in the Partial Linear Model
    (2016-04) Heyman, Megan
    As a tree grows, the trunk diameter increases, and in a typical year, a tree-ring is produced. The width of this ring reflects growing conditions during the year -- when standardized, a wider ring indicates better growing conditions. Thus, tree-rings contain yearly climatic information, such as precipitation and temperature. Tree-ring records exist for thousands of years in many locations across the earth, and a goal of paleoclimatologists is to use these records to understand past climate. A subset of records from the international tree-ring data bank (ITRDB) for Pinus ponderosa is introduced and analyzed in this talk. We specifically address what significant signals (long or short term) are included in this chronology. A newly proposed resampling technique, called the wild scale-enhanced bootstrap (WiSE bootstrap), is utilized in this analysis and implemented using the WiSEBoot R package. This methodology is based in a partial linear model where the nonparametric component is approximated by a wavelet basis. The WiSE bootstrap provides a model selection (in the basis dimension) and consistent parameter estimates. Additionally, the document includes an overview of all of our research results involving the partial linear model, bootstrap, and wavelets.
  • Thumbnail Image
    Item
    Modeling the Distribution of the Patients’ Stay at PACU
    (2020) Yang, Jingyi
    In a recent study to improve the patient flows at a local hospital, data of the patients’ length of stay at PACU need to be analyzed to fit into different theoretical distributions to be used in mathematical and simulation models. However, due to the irregularity of the data, normal software that comes with the simulation package cannot find a theoretical distribution for the data. In this project, we want to model the distribution of observed staying time statistically. Specifically, we consider a mixture model of two gamma distribution. We use expectation-maximization (EM) algorithm to find the parameter estimates and use bootstrap method to obtain the variance and consequently the confidence interval of the parameters.
  • Thumbnail Image
    Item
    R Code and Output Supporting: Resampling-Based Methods for Biologists
    (2020-03-02) Fieberg, John R; Vitense, Kelsey; Johnson, Douglas H; Jfieberg@umn.edu; Fieberg, John R
    This repository contains data, R code, and associated output from running R code supporting results reported in: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ [In Revision]