# Discussion of ‘Event history and topological data analysis’

Biometrika

Published On 2021/12/1

Although topological data analysis has been around for many decades with well-grounded theoretical development, it still suffers from numerous statistical and computational issues. For these reasons, it has not yet become a standard tool for data scientists. The authors point out the difficulty of directly applying existing statistical models to persistent homology due to the heterogeneous nature of topological features. The statistical development in topological data analysis in the last decade has been focused on making heterogeneous features into homogenous structured data by transformations or smoothing. Thus, the idea of applying survival analysis techniques to the birth and death process of topological features is very intriguing. The authors succeeded in elucidating the connection between event history methods and the lifetime of topological features, and the paper has stimulated many new interesting questions.

Journal

Biometrika

Published On

2021/12/1

Volume

108

Issue

4

Page

775-778

## Authors

#### Hernando Ombao

##### King Abdullah University of Science and Technology

Position

Professor of Statistics

H-Index(all)

46

H-Index(since 2020)

30

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Time Series

Signal Processing

Space-Time Models

Neuroscience

University Profile Page

#### Jesper Møller

##### Aalborg Universitet

Position

Professor in Statistics

H-Index(all)

46

H-Index(since 2020)

23

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Mathematical Statistics

Probability Theory

University Profile Page

#### Moo K. Chung

##### University of Wisconsin-Madison

Position

H-Index(all)

44

H-Index(since 2020)

32

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Computational Anatomy

Shape Analysis

Functional Data Analysis

Brain Connectivity

Topological Data Analysis

University Profile Page

#### Peter Bubenik

##### University of Florida

Position

Professor of Mathematics

H-Index(all)

19

H-Index(since 2020)

17

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Applied Topology

Topological Data Analysis

University Profile Page

#### Christophe Biscio

##### Aalborg Universitet

Position

H-Index(all)

10

H-Index(since 2020)

10

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Spatial Statistics

Topological Data Analysis

University Profile Page

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*2024/3/1*

Iván Díaz

Cornell University

Biometrika

##### Nonparametric efficient causal mediation with intermediate confounders (vol 108, pg 627, 2021)

Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence function in the nonparametric statistical model. We use the efficient influence function to develop two asymptotically optimal nonparametric estimators that leverage data-adaptive regression for the estimation of nuisance parameters: a one-step estimator and a targeted minimum loss estimator. We further present results establishing the conditions under which these estimators are consistent, multiply robust, -consistent and efficient. We illustrate the finite-sample performance of the estimators and corroborate our theoretical results in a simulation study. We also …

*2021/9/1*

Dehan Kong

University of Toronto

Biometrika

##### Promises of parallel outcomes

A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric, hence the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.

*2024/2/19*

Haojie Ren

Penn State University

Biometrika

##### Selective conformal inference with false coverage-statement rate control

Conformal inference is a popular tool for constructing prediction intervals. We consider here the scenario of post-selection/selective conformal inference, that is prediction intervals are reported only for individuals selected from unlabelled test data. To account for multiplicity, we develop a general split conformal framework to construct selective prediction intervals with the false coverage-statement rate control. We first investigate benjamini2005false's false coverage rate-adjusted method in the present setting, and show that it is able to achieve false coverage-statement rate control but yields uniformly inflated prediction intervals. We then propose a novel solution to the problem called selective conditional conformal prediction. Our method performs selection procedures on both the calibration set and test set, and then constructs conformal prediction intervals for the selected test candidates with the aid of …

*2024/2/20*

Aldo Solari

Università degli Studi di Milano-Bicocca

Biometrika

##### Flexible control of the median of the false discovery proportion

We introduce a multiple testing procedure that controls the median of the proportion of false discoveries in a flexible way. The procedure only requires a vector of p-values as input and is comparable to the Benjamini–Hochberg method, which controls the mean of the proportion of false discoveries. Our method allows free choice of one or several values of alpha after seeing the data, unlike the Benjamini–Hochberg procedure, which can be very anti-conservative when alpha is chosen post hoc. We prove these claims and illustrate them with simulations. Our procedure is inspired by a popular estimator of the total number of true hypotheses. We adapt this estimator to provide simultaneously median unbiased estimators of the proportion of false discoveries, valid for finite samples. This simultaneity allows for the claimed flexibility. Our approach does not assume independence. The time complexity of our method …

*2024/3/23*

Jonathan Terhorst

University of Michigan

Biometrika

##### A linear adjustment-based approach to posterior drift in transfer learning

We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii …

*2024/3/1*