Flexible Models for Complex Data with Applications
| Autor: | Domien Craens, Slađana Babić, Christophe Ley |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Complex data type Distribution (number theory) Computer science 05 social sciences Directional statistics Inference Statistical model 01 natural sciences 010104 statistics & probability Skewness 0502 economics and business Probability distribution Statistical physics 0101 mathematics Statistics Probability and Uncertainty 050205 econometrics |
| Zdroj: | Annual Review of Statistics and Its Application. 8:369-391 |
| ISSN: | 2326-831X 2326-8298 |
| Popis: | Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges. |
| Databáze: | OpenAIRE |
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