71 citations · 145 across the 18 of their papers we have counts for
19 papers
Compensator-based inference for signal detection under unknown background: the binned data case
Aritra Banerjee, Sara Algeri
The problem of signal detection under an unknown background can be framed as one of inferring the weight of a mixture model with one misspecified component. Banerjee and Algeri (20…
Compensator-Based Inference for Signal Detection Under Unknown Background
Aritra Banerjee, Sara Algeri
The problem of detecting new signals in the presence of an unknown background is ubiquitous in scientific discoveries and is especially prominent in the physical sciences. Most sol…
A scalable Bayesian framework for galaxy emission line detection and redshift estimation
Alexander Kuhn, Bonnabelle Zabelle, Sara Algeri +2
Estimating galaxy redshifts is crucial for constraining key physical quantities like those in the equation of state of dark energy. Modern telescopes such as the James Webb Space T…
A New Class of Asymptotically Distribution-Free Smooth Tests
Xiangyu Zhang, Sara Algeri
This article demonstrates how recent developments in the theory of empirical processes allow us to construct a new family of asymptotically distribution-free smooth tests. Their di…
On Validating Angular Power Spectral Models for the Stochastic Gravitational-Wave Background Without Distributional Assumptions
Xiangyu Zhang, Erik Floden, Hongru Zhao +4
It is demonstrated that estimators of the angular power spectrum commonly used for the stochastic gravitational-wave background (SGWB) lack a closed-form analytical expression for…
Testing models for angular power spectra: A distribution-free approach
Sara Algeri, Xiangyu Zhang, Erik Floden +4
A novel goodness-of-fit strategy is introduced for testing models of angular power spectra with unknown parameters. Using this strategy, it is possible to assess the validity of su…
On the statistical analysis of grouped data: when Pearson and other divisible statistics are not goodness-of-fit tests
Sara Algeri, Estate V. Khmaladze
Thousands of experiments are analyzed, and papers are published each year involving the statistical analysis of grouped data. While this area of statistics is often perceived -- so…
A novel approach to detect line emission under high background in high-resolution X-ray spectra
Xiangyu Zhang, Sara Algeri, Vinay Kashyap +1
We develop a novel statistical approach to identify emission features or set upper limits in high-resolution spectra in the presence of high background. The method relies on detect…
Sequential hypothesis testing for Axion Haloscopes
Andrea Gallo Rosso, Sara Algeri, Jan Conrad
The goal of this paper is to introduce a novel likelihood-based inferential framework for axion haloscopes which is valid under the commonly applied "rescanning" protocol. The prop…
Estimating the lifetime risk of a false positive screening test result
Tim White, Sara Algeri
False positive results in screening tests have potentially severe psychological, medical, and financial consequences for the recipient. However, there have been few efforts to quan…
K-2 rotated goodness-of-fit for multivariate data
Sara Algeri
Consider a set of multivariate distributions, , aiming to explain the same phenomenon. For instance, each may correspond to a different candidate background mo…
Informative Goodness-of-Fit for Multivariate Distributions
Sara Algeri
This article introduces an informative goodness-of-fit (iGOF) approach to study multivariate distributions. When the null model is rejected, iGOF allows us to identify the underlyi…
Exhaustive goodness-of-fit via smoothed inference and graphics
Sara Algeri, Xiangyu Zhang
Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. Given their inferential nature, they can be considered a crucial st…
Searching for new physics with profile likelihoods: Wilks and beyond
Sara Algeri, Jelle Aalbers, Knut Dundas Morå +1
Particle physics experiments use likelihood ratio tests extensively to compare hypotheses and to construct confidence intervals. Often, the null distribution of the likelihood rati…
Detecting new signals under background mismodelling
Sara Algeri
Searches for new astrophysical phenomena often involve several sources of non-random uncertainties which can lead to highly misleading results. Among these, model-uncertainty arisi…
Statistical challenges in the search for dark matter
Sara Algeri, Melissa van Beekveld, Nassim Bozorgnia +19
The search for the particle nature of dark matter has given rise to a number of experimental, theoretical and statistical challenges. Here, we report on a number of these statistic…
Testing One Hypothesis Multiple Times: The Multidimensional Case
Sara Algeri, David A. van Dyk
The identification of new rare signals in data, the detection of a sudden change in a trend, and the selection of competing models, are among the most challenging problems in stati…
Testing One Hypothesis Multiple times
Sara Algeri, David A. van Dyk
In applied settings, tests of hypothesis where a nuisance parameter is only identifiable under the alternative often reduces into one of Testing One Hypothesis Multiple times (TOHM…
A method for comparing non-nested models with application to astrophysical searches for new physics
Sara Algeri, Jan Conrad, David A. van Dyk
Searches for unknown physics and decisions between competing astrophysical models to explain data both rely on statistical hypothesis testing. The usual approach in searches for ne…