# Probability Models for Ranking Data

• Mayer Alvo
• Philip L. H. Yu
Chapter
Part of the Frontiers in Probability and the Statistical Sciences book series (FROPROSTAS)

## Abstract

Probability modeling for ranking data is an efficient way to understand people’s perception and preference on different objects. Various probability models for ranking data have been developed, particularly in the last decade where many new problems involving a large number of objects emerged. In their review paper on probability models for ranking data, Critchlow et al. (1991) broadly categorized these models into four classes: (1) order statistics models, (2) paired comparison models, (3) distance-based models, and (4) multistage models. Since their publication in 1991, variants of these models and new models have been developed. In this chapter, we will introduce these four classes of models and describe their properties.

## Keywords

Ranking Model Ranking Data Mixed Logit Model Ranking Probability Complete Consensus
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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