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Universal inference with composite likelihoods

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Hien D Nguyen, Jessica Bagnall-Guerreiro, Andrew T Jones

Methodology · Submitted 28 Jun 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We demonstrate that generic composite likelihoods consisting of marginal and conditional specifications permit the simple construction of composite likelihood ratio-like statistics from which finite-sample valid confidence sets and hypothesis tests can be constructed.

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arXiv ID2106.14399 (version 1)
Pith IDZMKALS6F, Pith's permanent record of this paper and its review
CitationAndrew T Jones, Hien D Nguyen, Jessica Bagnall-Guerreiro. "Universal inference with composite likelihoods." arXiv:2106.14399 (2021). https://pith.science/paper/ZMKALS6F
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,587 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.14 · human participants 0.02 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.26 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Maximum composite likelihood estimation is a useful alternative to maximum likelihood estimation when data arise from data generating processes (DGPs) that do not admit tractable joint specification. We demonstrate that generic composite likelihoods consisting of marginal and conditional specifications permit the simple construction of composite likelihood ratio-like statistics from which finite-sample valid confidence sets and hypothesis tests can be constructed. These statistics are universal in the sense that they can be constructed from any estimator for the parameter of the underlying DGP. We demonstrate our methodology via a simulation study using a pair of conditionally specified bivariate models.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Maximum composite likelihood estimation is a useful alternative to maximum likelihood estimation when data arise from data generating processes (DGPs) that do not admit tractable joint specification. background 1.00 0.05 0.05 asserted 1.00
s1 We demonstrate that generic composite likelihoods consisting of marginal and conditional specifications permit the simple construction of composite likelihood ratio-like statistics from which finite-sample valid confidence sets and hypothesis tests can be constructed. contribution 0.82 0.94 0.68 asserted 1.00
s2 These statistics are universal in the sense that they can be constructed from any estimator for the parameter of the underlying DGP. interpretation 0.59 0.63 0.43 asserted 1.00
s3 We demonstrate our methodology via a simulation study using a pair of conditionally specified bivariate models. method 0.69 0.95 0.04 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from simulation or computation 0.93.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Maximum composite likelihood estimation is a useful alternative to maximum likelihood estimation when data arise from data generating processes (DGPs) that do not admit tractable joint specification. We demonstrate that generic composite likelihoods consisting of marginal and conditional specifications permit the simple construction of composite likelihood ratio-like statistics from which finite-sample valid confidence sets and hypothesis tests can be constructed. These statistics are universal in the sense that they can be constructed from any estimator for the parameter of the underlying DGP. We demonstrate our methodology via a simulation study using a pair of conditionally specified bivariate models.

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