Journal of Statistics: Advances in Theory and ApplicationsAuthor's: Lahiru Wickramasinghe
Pages: [1] - [11]
Received Date: August 13, 2024
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100122307
Sparse data, particularly in the form of sampling zeros or categories with very low counts, pose significant challenges to traditional estimation methods, often leading to biased parameter estimates, reduced statistical power, and unreliable conclusions. The pervasive nature of sparse multinomial data across various disciplines, including genetics, ecology, and the social sciences, underscores the urgent need for improved analytical techniques. This review paper highlights the critical importance of developing methods that can more accurately and robustly handle sparse data. By effectively managing zeros and low counts, these advanced techniques offer a more accurate representation of underlying distributions, thereby enhancing the validity of statistical inferences. Such improvements are crucial for informed decision-making and sound policy formulation across multiple fields of study.
Dirichlet distribution, multinomial distribution, Bayesian.
