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52621 results in Statistics and Probability

Page 63 of 2632

  • 4 - Spectral Analysis

  • Oliver Linton, University of Cambridge
  • Book:
    Time Series for Economics and Finance
    Published online:
    19 December 2024
    Print publication:
    19 December 2024, pp 102-128

  • Summary

    This chapter introduces the frequency-domain view and how this way of thinking can help with understanding periodic behavior and cycles. We define the spectral density function and how commonly used filters affect the spectral shape. We discuss estimation by the periodogram and smoothing methods.

  • 2 - Stationarity and Mixing

  • Oliver Linton, University of Cambridge
  • Book:
    Time Series for Economics and Finance
    Published online:
    19 December 2024
    Print publication:
    19 December 2024, pp 14-41

  • Summary

    This chapter introduces more formal concepts like stationarity and mixing, and explains why they are needed. We also define the autocorrelation function and describe its properties and how it is estimated from sample data. We discuss the properties of the estimator of the mean and autocorrelation, and how they can be used to conduct statistical inference.

  • 1 - Introduction

  • Oliver Linton, University of Cambridge
  • Book:
    Time Series for Economics and Finance
    Published online:
    19 December 2024
    Print publication:
    19 December 2024, pp 1-13

  • Summary

    This chapter introduces what a time series is and defines the important decomposition into trend, seasonal, and cycle that guides our thinking. We introduce a number of datasets used in the book and plot them to show their key features in terms of these components.

  • Lessons learned and real-world insights from implementing neutralizing monoclonal antibody programs for treatment of COVID-19

  • Nalini Ambrose, Alpesh N. Amin, Brian Anderson, Adam Aten, Brian Canter, Daniel Chow, Matthew D’Ambrosio, Risa Danan, Ashley L. Drews, Fraser W. Gaspar, Rachele Hendricks-Sturrup, John O’Horo, Raymund R. Razonable, Seth Roberts, Colin Shepard, Farhaan Vahidy, Brandon Webb, Jennifer Yttri
  • Journal:
    Data & Policy / Volume 6 / 2024
    Published online by Cambridge University Press:
    19 December 2024, e76
    • Article
      • You have access
      • Open access
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  • Coronavirus disease-2019 precipitated the rapid deployment of novel therapeutics, which led to operational and logistical challenges for healthcare organizations. Four health systems participated in a qualitative study to abstract lessons learned, challenges, and promising practices from implementing neutralizing monoclonal antibody (nMAb) treatment programs. Lessons are summarized under three themes that serve as critical building blocks for health systems to rapidly deploy novel therapeutics during a pandemic: (1) clinical workflows, (2) data infrastructure and platforms, and (3) governance and policy. Health systems must be sufficiently agile to quickly scale programs and resources in times of uncertainty. Real-time monitoring of programs, policies, and processes can help support better planning and improve program effectiveness. The lessons and promising practices shared in this study can be applied by health systems for distribution of novel therapeutics beyond nMAbs and toward future pandemics and public health emergencies.

Page 63 of 2632