Results 11 to 20 of about 25,561,218 (304)
Clustering Algorithm for Zero-Inflated Data [PDF]
, 2020 Zero-inflated data are common in biomedical research. In cluster analysis, the heuristic approach fails to provide inferential properties to the outcome while the existing model-based approach only works in the case of a mixture of multivariate normal.
Thanataveerat, Anusorn
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A Poisson-Gamma Model for Zero Inflated Rainfall Data [PDF]
Journal of Probability and Statistics, 2018 Rainfall modeling is significant for prediction and forecasting purposes in agriculture, weather derivatives, hydrology, and risk and disaster preparedness.
Nelson Christopher Dzupire +2 more
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Zero-Inflated Data Clustering Using Graph Neural Networks with Zero-Inflated Likelihood
StatsSparse count data, such as patent document–keyword matrices, often contain excessive zeros and overdispersion, making conventional distance-based clustering methods less suitable.
Sunghae Jun
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Zero inflated high dimensional compositional data with DeepInsight.
PLoS ONEThrough the Human Microbiome Project, research on human-associated microbiomes has been conducted in various fields. New sequencing techniques such as Next Generation Sequencing (NGS) and High-Throughput Sequencing (HTS) have enabled the inclusion of a ...
Jeseok Lee, Byungwon Kim
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Compositional zero-inflated network estimation for microbiome data [PDF]
BMC Bioinformatics, 2020 Background The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome.
Min Jin Ha +4 more
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Multiple imputation of incomplete zero‐inflated count data
Statistica Neerlandica, 2013 Empirical count data are often zero‐inflated and overdispersed. Currently, there is no software package that allows adequate imputation of these data. We present multiple‐imputation routines for these kinds of count data based on a Bayesian regression approach or alternatively based on a bootstrap approach that work as add‐ons for the popular multiple ...
Kleinke, Kristian, Reinecke, Jost
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A comparison of zero-inflated and hurdle models for modeling zero-inflated count data [PDF]
Journal of Statistical Distributions and Applications, 2021 Abstract Counts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a ...
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Zero-Inflated Ordinal Mixed-Effects Models [PDF]
, 2022 Excess zeros issues are very common in count data. Furthermore, when different type of zero sources are suspected in the data, zero-inflated models, such as zero-inflated Poisson model and zero-inflated Negative Binomial model, are commonly used in ...
Naijun Chen (2720566)
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A Zero‐Inflated Regression Model for Grouped Data [PDF]
Oxford Bulletin of Economics and Statistics, 2014 AbstractWe introduce the (panel) zero‐inflated interval regression (ZIIR) model, which is ideally suited when data are in the form of groups, and there is an ‘excess’ of zero observations. We apply our new modelling framework to the analysis of visits to the general practitioner (GP) using individual‐level data from the British Household Panel Survey ...
Brown, S. +4 more
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Marginal Mean Models for Zero-Inflated Count Data [PDF]
Biometrics, 2016 Summary Zero-inflated regression models have emerged as a popular tool within the parametric framework to characterize count data with excess zeros. Despite their increasing popularity, much of the literature on real applications of these models has centered around the latent class formulation where the mean response of the so-called at ...
Todem, David +2 more
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