Description: Zero-Inflated Models are a set of statistical techniques designed to address situations where count data exhibit an excessive number of zeros. These models are particularly useful in contexts where the data are not only discrete but also show a skewed distribution towards zero, which can complicate conventional analysis. Unlike standard count models, which assume that zeros result from a normal counting process, zero-inflated models recognize that the presence of zeros may stem from a different underlying mechanism, such as the absence of an event or data censoring. This allows analysts to obtain more accurate and meaningful estimates by modeling the probability of an event occurring and the number of times it occurs, rather than simply counting events. In general, these models are used in various fields such as economics, healthcare, and environmental studies to enhance prediction and data analysis, enabling systems to learn more effectively from complex datasets that include a high proportion of zeros. Their implementation can range from zero-inflated Poisson regression to more complex models that combine different distributions to capture the nature of the data.
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