Harvimtilastot

Harvimtilastot is a Finnish term used to describe statistical methods and analyses designed for sparse data, where observations are limited, many counts are zero, or events are rare. The concept appears in Finnish statistical literature as a practical label for dealing with computational and inferential challenges posed by small samples and sparse occurrences.

Methodologically, harvimtilastot encompasses approaches such as zero-inflated and hurdle models for count data, Poisson or negative binomial regression with adjustments for sparsity and overdispersion, exact or conditional inference methods suitable for small samples, and Bayesian hierarchical or penalized likelihood methods that borrow strength across groups.

Applications include epidemiology for rare diseases, ecology for rare species sightings, criminology for infrequent offenses, finance for rare defaults, and other fields where events occur infrequently. Careful data handling is required, including attention to sampling design, weighting, potential small-sample bias in maximum likelihood estimates, and the risk of overfitting when models are complex relative to available data.

The term harvimtilastot does not have a single universally accepted definition in English-language literature; its closest equivalents are sparse data analysis, rare-event statistics, zero-inflated models and hurdle models. In Finnish practice, it is often described using these linked concepts rather than as a strict, standalone subfield.