About

Thomas J. Diciccio is an Associate Professor in the Department of Statistics and Data Science at the School of Industrial and Labor Relations, Cornell University. He has held faculty positions at McMaster University, the University of Toronto, and Stanford University prior to his current appointment. His teaching primarily focuses on statistical methodology for the social sciences at the undergraduate level. His research broadly involves higher-order methods for accurate statistical inference, achieved through asymptotic, computer-intensive, and Bayesian methods. His areas of application include linear models, skew distributions, and time series, contributing to the development of statistical techniques for social science research.

Research topics

• Virology
• Mathematics education
• Medicine
• Computer Science
• Psychology
• Medical education
• Mathematics
• Algorithm
• Engineering
• Statistics

Selected publications

• Confidence Intervals for Seroprevalence

  Statistical Science · 2022-06-22 · 7 citations

  article1st authorCorresponding

  This paper concerns the construction of confidence intervals in standard seroprevalence surveys. In particular, we discuss methods for constructing confidence intervals for the proportion of individuals in a population infected with a disease using a sample of antibody test results and measurements of the test’s false positive and false negative rates. We begin by documenting erratic behavior in the coverage probabilities of standard Wald and percentile bootstrap intervals when applied to this problem. We then consider two alternative sets of intervals constructed with test inversion. The first set of intervals are approximate, using either asymptotic or bootstrap approximation to the finite-sample distribution of a chosen test statistic. We consider several choices of test statistic, including maximum likelihood estimators and generalized likelihood ratio statistics. We show with simulation that, at empirically relevant parameter values and sample sizes, the coverage probabilities for these intervals are close to their nominal level and are approximately equi-tailed. The second set of intervals are shown to contain the true parameter value with probability at least equal to the nominal level, but can be conservative in finite samples.

  PublisherDOI

• Confidence Intervals for Seroprevalence

  arXiv (Cornell University) · 2021-03-27

  preprintOpen access1st authorCorresponding

  This paper concerns the construction of confidence intervals in standard seroprevalence surveys. In particular, we discuss methods for constructing confidence intervals for the proportion of individuals in a population infected with a disease using a sample of antibody test results and measurements of the test's false positive and false negative rates. We begin by documenting erratic behavior in the coverage probabilities of standard Wald and percentile bootstrap intervals when applied to this problem. We then consider two alternative sets of intervals constructed with test inversion. The first set of intervals are approximate, using either asymptotic or bootstrap approximation to the finite-sample distribution of a chosen test statistic. We consider several choices of test statistic, including maximum likelihood estimators and generalized likelihood ratio statistics. We show with simulation that, at empirically relevant parameter values and sample sizes, the coverage probabilities for these intervals are close to their nominal level and are approximately equi-tailed. The second set of intervals are shown to contain the true parameter value with probability at least equal to the nominal level, but can be conservative in finite samples.

  PublisherOA PDFDOI

• Learning during the COVID-19 pandemic: It is not who you teach, but how you teach

  Economics Letters · 2021 · 150 citations

  • Computer Science
  • Psychology
  • Medical education
  PublisherOA PDFDOI

• Exact tests via multiple data splitting

  Statistics & Probability Letters · 2020 · 33 citations

  • Mathematics
  • Statistics
  • Algorithm
  PublisherDOI

• Learning during the COVID-19 Pandemic: It Is Not Who You Teach, but How You Teach

  RePEc: Research Papers in Economics · 2020-01-01 · 1 citations

  preprint

  We use standardized end-of-course knowledge assessments to examine student learning during the disruptions induced by the COVID-19 pandemic. Examining seven economics courses taught at four US R1 institutions, we find that students performed substantially worse, on average, in Spring 2020 when compared to Spring or Fall 2019. We find no evidence that the effect was driven by specific demographic groups. However, our results suggest that teaching methods that encourage active engagement, such as the use of small group activities and projects, played an important role in mitigating this negative effect. Our results point to methods for more effective online teaching as the pandemic continues.

  Publisher

• Learning During the COVID-19 Pandemic: It is Not Who You Teach, But How You Teach

  SSRN Electronic Journal · 2020 · 20 citations

  • Mathematics education
  • Medical education
  • Psychology
  PublisherDOI

• Learning During the COVID-19 Pandemic: It Is Not Who You Teach, but How You Teach

  National Bureau of Economic Research · 2020-10-01 · 24 citations

  reportOpen access

  We use standardized end-of-course knowledge assessments to examine student learning during the disruptions induced by the COVID-19 pandemic. Examining seven economics courses taught at four US R1 institutions, we find that students performed substantially worse, on average, in Spring 2020 when compared to Spring or Fall 2019. We find no evidence that the effect was driven by specific demographic groups. However, our results suggest that teaching methods that encourage active engagement, such as the use of small group activities and projects, played an important role in mitigating this negative effect. Our results point to methods for more effective online teaching as the pandemic continues.

  PublisherDOI

• DIFFUSION OF INNOVATION IN SUSTAINABLE BUILDING PRACTICES AND THE ROLE OF STAKEHOLDERS

  Journal of Green Building · 2018-01-01 · 8 citations

  articleSenior author

  ABSTRACT The stakeholder network in a building project can influence the process of adopting sustainable building practice. Complexity of construction projects calls for integrated modes of collaboration, while the excess inertia among stakeholders resulted in sluggish adoption of sustainable design and technologies. This study examined buildings that both had and had not adopted Leadership in Energy and Environmental Design and/or ENERGY STAR in the New York metropolitan area, built, or went through major renovation between 1998 and 2013. Secondary datasets from multiple sources, including a private building database company, US Green Building Council, and the US Environmental Protection Agency, were combined based on building address and used for analysis. Stakeholders involved in those projects were retrospectively identified to understand the diffusion of innovation. The analysis included a total of 205 projects and 273 organizations. Findings suggest that having an architect who had worked on ENERGY STAR project(s) increased the likelihood of adopting ENERGY STAR. However, stakeholders' previous work collaboration was not associated with the adoption of sustainable programs. The method of utilizing multiple secondary datasets was tested to contribute to the methodology of building research by enabling the accumulation of knowledge.

  PublisherDOI

• Testing for sub-models of the skew t-distribution

  Statistical Methods & Applications · 2017-07-08 · 5 citations

  article1st author
  PublisherDOI

• The formal relationship between analytic and bootstrap approaches to parametric inference

  Journal of Statistical Planning and Inference · 2017-06-02 · 2 citations

  article1st author
  PublisherDOI

Recent grants

• Higher-Order and Exact Methods for Statistical Inference

  NSF · $152k · 2002–2007

Frequent coauthors

• G. A. Young

  32 shared

• Anna Clara Monti

  University of Sannio

  22 shared

• Joseph P. Romano

  Stanford University

  18 shared

• Todd A. Kuffner

  17 shared

• Michael A. Martin

  IVL Swedish Environmental Research Institute

  14 shared

• Mary E. Thompson

  University of Waterloo

  9 shared

• D.V. Nydam

  Cornell University

  6 shared

• P.A. Ospina

  6 shared