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Fast likelihood-based inference for latent count models using the saddlepoint approximation
W Zhang1, M V Bravington2, R M Fewster1
1Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand.
Biometrics
|January 29, 2019
Summary
This study introduces a fast maximum-likelihood method for latent count models using saddlepoint approximations. This approach offers accurate inference, even with small observed counts, improving upon computationally intensive sampling methods.
Area of Science:
- Statistics
- Computational Statistics
Background:
- Latent count models involve unobserved count data summarized into observed data.
- Current inference relies on slow Bayesian sampling methods.
- These models are used in population estimation, contingency table analysis, and network flow analysis.
Purpose of the Study:
- To develop a novel, efficient maximum-likelihood approach for latent count models.
- To overcome the computational limitations of existing stochastic inference methods.
- To provide fast and accurate inference for complex count data.
Main Methods:
- Utilized saddlepoint approximations to construct likelihoods.
- Developed an efficient maximization algorithm for the saddlepoint likelihood.
- Validated the method on multinomial distribution cases and compared with existing approaches.
Main Results:
- The saddlepoint approximation method provides fast and accurate inference.
- The method is effective even when observed counts are small.
- Demonstrated efficient computation for large-scale problems.
Conclusions:
- The saddlepoint likelihood approach offers a significant improvement for latent count model inference.
- This novel method is computationally efficient and robust.
- Enables wider application of latent count models in various scientific fields.
Keywords:
capture-recapturecontingency tablelatent count modelmulti-list methodpopulation size estimatesaddlepoint approximationMore Related Videos
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