observations of X and Y with the current values of X to generate an assessment of Y is similarity-weighted averaging. It suggests … of Y_{i}, for every i =1,...,n, is the similarity between the vector X_{p}^{1},...,X_{p}^{m}, associated with Y_{p}, and … are determined by the proximity to a similarity-weighted average for a certain similarity function. The axiomatization …

of beliefs given each of the databases, the belief formation process follows a simple formula: beliefs are a similarity …

the current values of x to generate an assessment of y is similarity-weighted averaging. It suggests that the predicted … similarity between the vector x_{n+1}^{1},...,x_{n+1}^{m}, associated with y_{n+1}, and the previously observed vector, x_{i}^{1 …},...,x_{i}^{m}. This paper axiomatizes, in terms of the prediction y_{n+1}, a similarity function that is a (decreasing …

A time-varying autoregression is considered with a similarity-based coefficient and possible drift. It is shown that … the random walk model has a natural interpretation as the leading term in a small-sigma expansion of a similarity model … with an exponential similarity function as its autoregressive coefficient. Consistency of the quasi-maximum likelihood …