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1. Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models

   https://doi.org/10.1186/s40488-021-00117-0  

   Zou, Yixuan; Hannig, Jan; Young, Derek S. (December 2021, Journal of Statistical Distributions and Applications)

   null (Ed.)

   Abstract Zero-inflated and hurdle models are widely applied to count data possessing excess zeros, where they can simultaneously model the process from how the zeros were generated and potentially help mitigate the effects of overdispersion relative to the assumed count distribution. Which model to use depends on how the zeros are generated: zero-inflated models add an additional probability mass on zero, while hurdle models are two-part models comprised of a degenerate distribution for the zeros and a zero-truncated distribution. Developing confidence intervals for such models is challenging since no closed-form function is available to calculate the mean. In this study, generalized fiducial inference is used to construct confidence intervals for the means of zero-inflated Poisson and Poisson hurdle models. The proposed methods are assessed by an intensive simulation study. An illustrative example demonstrates the inference methods. 

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2. MONET: Multiview Semi-supervised Keypoint via Epipolar Divergence

   https://doi.org/10.1109/ICCV.2019.00084  

   Yao, Y.; Jafarian, Y.; Park, H.S. (January 2019, International conference on Computer Vision)

   This paper presents MONET -- an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise the unlabeled data, integrating the geometry into learning a keypoint detector is challenging due to representation mismatch. We address this mismatch by formulating a new differentiable representation of the epipolar constraint called epipolar divergence---a generalized distance from the epipolar lines to the corresponding keypoint distribution. Epipolar divergence characterizes when two view keypoint distributions produce zero reprojection error. We design a twin network that minimizes the epipolar divergence through stereo rectification that can significantly alleviate computational complexity and sampling aliasing in training. We demonstrate that our framework can localize customized keypoints of diverse species, e.g., humans, dogs, and monkeys. 

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3. A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data

   https://doi.org/10.1111/biom.13853  

   Koslovsky, Matthew D. (March 2023, Biometrics)

   Abstract The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the data as well as overdispersion. A major limitation of the DM distribution is that it is unable to handle excess zeros typically found in practice which may bias inference. To fill this gap, we propose a novel Bayesian zero-inflated DM model for multivariate compositional count data with excess zeros. We then extend our approach to regression settings and embed sparsity-inducing priors to perform variable selection for high-dimensional covariate spaces. Throughout, modeling decisions are made to boost scalability without sacrificing interpretability or imposing limiting assumptions. Extensive simulations and an application to a human gut microbiome dataset are presented to compare the performance of the proposed method to existing approaches. We provide an accompanying R package with a user-friendly vignette to apply our method to other datasets. 

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4. Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models

   https://doi.org/10.1145/3534678.3539464  

   Wilson, Tyler; McDonald, Andrew; Galib, Asadullah Hill; Tan, Pang-Ning; Luo, Lifeng (August 2022, KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   Zhang, Aidong; Rangwala, Huzefa (Ed.)

   Zero-inflated, heavy-tailed spatiotemporal data is common across science and engineering, from climate science to meteorology and seismology. A central modeling objective in such settings is to forecast the intensity, frequency, and timing of extreme and non-extreme events; yet in the context of deep learning, this objective presents several key challenges. First, a deep learning framework applied to such data must unify a mixture of distributions characterizing the zero events, moderate events, and extreme events. Second, the framework must be capable of enforcing parameter constraints across each component of the mixture distribution. Finally, the framework must be flexible enough to accommodate for any changes in the threshold used to define an extreme event after training. To address these challenges, we propose Deep Extreme Mixture Model (DEMM), fusing a deep learning-based hurdle model with extreme value theory to enable point and distribution prediction of zero-inflated, heavy-tailed spatiotemporal variables. The framework enables users to dynamically set a threshold for defining extreme events at inference-time without the need for retraining. We present an extensive experimental analysis applying DEMM to precipitation forecasting, and observe significant improvements in point and distribution prediction. All code is available at https://github.com/andrewmcdonald27/DeepExtremeMixtureModel. 

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5. A bias correction method in meta‐analysis of randomized clinical trials with no adjustments for zero‐inflated outcomes

   https://doi.org/10.1002/sim.9161  

   Zhou, Zhengyang; Xie, Minge; Huh, David; Mun, Eun‐Young (September 2021, Statistics in Medicine)

   Summary Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations. 

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