Jinzhou Li


Last Name

Li

First Name

Jinzhou

ORCID

0000-0002-4054-4104

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2022 - 20244
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Publications 1 - 4 of 4
  • Simultaneous false discovery proportion bounds via knockoffs and closed testing
    Item type: Journal Article
    Li, Jinzhou; Maathuis, Marloes H.; Goeman, Jelle J. (2024)
    Journal of the Royal Statistical Society Series B: Statistical Methodology
    We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's k-familywise error rate control method and interpolation. We then generalize it by considering a collection of k values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local test statistic. This allows us to obtain a further uniform improvement and other generalizations over previous methods. We also develop an efficient shortcut for its implementation. We compare the performance of our proposed methods in simulations and apply them to a data set from the UK Biobank.
  • Error-Controlled Statistical Learning and Topics in Causality
    Item type: Doctoral Thesis
    Li, Jinzhou (2022)
  • Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions
    Item type: Journal Article
    Deuber, David; Li, Jinzhou; Engelke, Sebastian; et al. (2024)
    Journal of the American Statistical Association
    Causal inference for extreme events has many potential applications in fields such as climate science, medicine, and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range. We introduce an estimator of the extremal quantile treatment effect that relies on asymptotic tail approximation, and use a new causal Hill estimator for the extreme value indices of potential outcome distributions. We establish asymptotic normality of the estimators and propose a consistent variance estimator to achieve valid statistical inference. We illustrate the performance of our method in simulation studies, and apply it to a real dataset to estimate the extremal quantile treatment effect of college education on wage. Supplementary materials for this article are available online.
  • Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
    Item type: Journal Article
    Huisman, Jana; Scire, Jérémie; Angst, Daniel C.; et al. (2022)
    eLife
    (JSH); Abstract The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS- CoV- 2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID- 19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS- CoV- 2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.
Publications 1 - 4 of 4