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Conditional Distribution Specification Testing Based on Data-Dependent Partitions

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Miguel A. Delgado, Julius Vainora

Econometrics · Submitted 2 Oct 2022

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

The authors' main claim Monte Carlo simulations indicate accurate size control and favorable power relative to existing bootstrap-based tests, particularly in higher-dimensional settings.

how it is stated · hedged possibility 0.92 read the record →

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arXiv ID2210.00624 (version 6)
Pith IDXOIDT3NC, Pith's permanent record of this paper and its review
CitationJulius Vainora, Miguel A. Delgado. "Conditional Distribution Specification Testing Based on Data-Dependent Partitions." arXiv:2210.00624 (2022). https://pith.science/paper/XOIDT3NC
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,858 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.13 · human participants 0.03 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.92 · compares with prior results 0.92 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v6. Results and the main claim are underlined; .

This article introduces a Pearson-type goodness-of-fit test for the parametric specification of conditional distribution models with continuous responses. Under correct specification, the Rosenblatt transform is uniformly distributed on $[0,1]$ conditionally on the explanatory variables. The test exploits this characterization by cross-classifying the transformed observations and the explanatory variables according to partitions of $[0,1]$ and their support, respectively. The resulting Pearson statistic has a chi-squared limiting distribution with known degrees of freedom and detects local alternatives converging to the null at the $n^{-1/2}$ rate. These results remain valid for the class of data-dependent partitions considered. Monte Carlo simulations indicate accurate size control and favorable power relative to existing bootstrap-based tests, particularly in higher-dimensional settings.

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 This article introduces a Pearson-type goodness-of-fit test for the parametric specification of conditional distribution models with continuous responses. contribution 1.00 0.87 0.20 asserted 0.99
s1 Under correct specification, the Rosenblatt transform is uniformly distributed on $[0,1]$ conditionally on the explanatory variables. background 0.99 0.07 0.04 asserted 1.00
s2 The test exploits this characterization by cross-classifying the transformed observations and the explanatory variables according to partitions of $[0,1]$ and their support, respectively. method 0.99 0.67 0.06 asserted 1.00
s3 The resulting Pearson statistic has a chi-squared limiting distribution with known degrees of freedom and detects local alternatives converging to the null at the $n^{-1/2}$ rate. result 1.00 0.85 0.37 asserted 1.00
s4 These results remain valid for the class of data-dependent partitions considered. result 0.95 0.77 0.13 asserted 1.00
s5 Monte Carlo simulations indicate accurate size control and favorable power relative to existing bootstrap-based tests, particularly in higher-dimensional settings. result 1.00 0.94 0.31 hedged possibility 0.92

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 0.79.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

This article introduces a Pearson-type goodness-of-fit test for the parametric specification of conditional distribution models with continuous responses. Under correct specification, the Rosenblatt transform is uniformly distributed on $[0,1]$ conditionally on the explanatory variables. The test exploits this characterization by cross-classifying the transformed observations and the explanatory variables according to partitions of $[0,1]$ and their support, respectively. The resulting Pearson statistic has a chi-squared limiting distribution with known degrees of freedom and detects local alternatives converging to the null at the $n^{-1/2}$ rate. These results remain valid for the class of data-dependent partitions considered. Monte Carlo simulations indicate accurate size control and favorable power relative to existing bootstrap-based tests, particularly in higher-dimensional settings.

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