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This guide provides comprehensive rules for authors revising technical papers for top AI/Statistics conferences like AISTATS. It emphasizes achieving statistical rigor, explicitly stating assumptions, pairing every claim with proof or evidence, ensuring conciseness (e.g., adhering to an 8-page limit), and maintaining double-blind academic style. Perfect for PhD students or researchers preparing manuscripts for highly technical review.

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

Provides comprehensive guidelines for statistical analysis and reporting in biological manuscripts, particularly in cancer research. It teaches researchers how to correctly define 'n' (distinguishing biological from technical replicates), select appropriate statistical tests (ANOVA, survival analysis, etc.), and manage multiple comparisons, ensuring high scientific rigor and reproducibility in published data.

This comprehensive checklist guides authors through the final, critical pre-submission review phase for high-impact scientific journals. It covers technical completeness, scientific rigor (e.g., statistics, cell line authentication), ethical compliance (e.g., IACUC/IRB), data availability, and formatting consistency, ensuring the manuscript is ready for final submission.

A detailed guide for authors structuring the 'Related Work' section of theoretical machine learning papers. It shifts focus from qualitative prose to quantitative comparison, instructing users to build explicit tables comparing prior bounds versus new bounds, and systematically covering diverse literature lanes like complexity theory, statistics, and open problems to rigorously establish novelty.

Provides rigorous guidelines for conducting robust, verifiable, and reproducible evaluations in networking research. It emphasizes moving beyond simple simulations by demanding the use of real target platforms, documenting full system provenance (traces and configs), establishing honestly tuned baselines, and reporting detailed measurement statistics and uncertainty. Essential for authors aiming for top-tier conference submissions.

This comprehensive guide helps researchers revise NLP research papers for top-tier conferences like EACL. It focuses on structuring the argument for fast legibility by emphasizing a clear Task-Gap-Method-Result-Scope arc, quantifying results with statistics, performing detailed error analysis, and maintaining academic rigor, anonymity, and multilingual clarity. Ideal for polishing conference submissions.

A comprehensive guide to the rigorous theoretical foundations required in advanced econometrics and theoretical statistics. It covers proving identification conditions (parameter uniqueness), deriving limiting distributions (asymptotics), and establishing existence and uniqueness from fundamental axioms. Essential for manuscripts requiring deep methodological proof, particularly for top-tier academic journals.

A comprehensive guide for structuring, auditing, and writing mathematically rigorous proofs for advanced academic papers, particularly in econometrics and statistics. It covers everything from defining assumptions and stating self-contained theorems to applying advanced proof techniques (e.g., fixed-point theorems, CLT). Use this when your central result requires impeccable rigor and completeness for top-tier publication.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

A comprehensive guide for executing and reporting rigorous data analysis for Environmental Science & Technology (ES&T) manuscripts. It details essential norms, including explicit QA/QC reporting (blanks, recoveries, LOD/LOQ), handling uncertainty, selecting appropriate statistics for environmental data (e.g., left-censored data), ensuring mass/energy balance closure, and guaranteeing reproducibility through master workflows.

This guide provides a detailed playbook for responding to major or minor revisions of scientific manuscripts, particularly in agronomy and crop science. It teaches authors how to structure the rebuttal letter, systematically address specific reviewer concerns (e.g., scope, statistics, reproducibility), and professionally reconcile conflicting feedback while protecting the core scientific contribution to facilitate publication acceptance.