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Adapting TabPFN for Zero-Inflated Metagenomic Data
This paper introduces a novel prior assumption for TabPFN—a meta-learning method designed to approximate Bayesian inference on synthetic datasets generated from a predefined prior—aimed at better accommodating the unique zero-inflated distributions characteristic of metagenomic data.Perciballi, Giulia +5 more
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Variable selection approach for zero-inflated count data via adaptive lasso
Journal of Applied Statistics, 2014Ping Zeng, Yongyue Wei, Yang Zhao
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Statistical inference for zero-and-one-inflated poisson models
Statistical Theory and Related Fields, 2017Yincai Tang, Ancha Xu
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Variable selection for distribution‐free models for longitudinal zero‐inflated count responses
Statistics in Medicine, 2016Tian Chen, Wan Tang, Hui Zhang
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Principal component analysis for zero-inflated compositional data
Computational Statistics & Data AnalysisKipoong Kim, Jaesung Park, Sungkyu Jung
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