• Corpus ID: 246276100

Zero-Truncated Poisson Regression for Zero-Inflated Multiway Count Data

@article{Lopez2022ZeroTruncatedPR,
  title={Zero-Truncated Poisson Regression for Zero-Inflated Multiway Count Data},
  author={O. Gonz'alez L'opez and Daniel M. Dunlavy and Richard B. Lehoucq},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.10014}
}

• O. L'opez, Daniel M. Dunlavy, R. Lehoucq
• Published 25 January 2022
• Mathematics, Computer Science
• ArXiv

. We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an N -way rank- R parametric tensor M ∈ (0 , ∞ ) I ×···× I generating Poisson observations can be accurately estimated from approximately IR 2 log 22 ( I ) non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known a priori .