Bayesian Inference for Kendall’s Rank Correlation Coefficient

Spearman Rank Correlation Coefficient

Local Rank Inference for Varying Coefficient Models.

By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This paper proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linea...

Spearman’s Rank Order Correlation Coefficient

• In this lesson, we will learn how to measure the coefficient of correlation for two sets of ranking. • The coefficient of correlation, r, measures the strength of association or correlation between two sets of data that can be measured. Sometimes, the data is not measurable but can only be ordered, as in ranking. • For example, two students can be asked to rank toast, cereals, and dim sum in ...

The Kendall Rank Correlation Coefficient

The Kendall (1955) rank correlation coefficient evaluates the degree of similarity between two sets of ranks given to a same set of objects. This coefficient depends upon the number of inversions of pairs of objects which would be needed to transform one rank order into the other. In order to do so, each rank order is represented by the set of all pairs of objects (e.g., [a,b] and [b,a] are the...

Bayesian inference for low-rank Ising networks

Estimating the structure of Ising networks is a notoriously difficult problem. We demonstrate that using a latent variable representation of the Ising network, we can employ a full-data-information approach to uncover the network structure. Thereby, only ignoring information encoded in the prior distribution (of the latent variables). The full-data-information approach avoids having to compute ...