Estimation.RUM.Nonparametric

full, top partial, or sub partial rank data

Data

iter

bandwidth, or smoothing parameter for KDE

Number of utility vector
  samples that we get per agent. More generally gives a
  more accurate estimate

utilities.per.agent

TRUE if data is sub partial, FALSE (default)
  if not

race


Given rank data (full, top partial, or sub partial), this
function returns an inference object that fits
nonparametric latent utilties on the rank data.


A set of methods to implement Generalized Method of Moments and Maximal
Likelihood methods for Random Utility Models. These methods are meant to
provide inference on rank comparison data. These methods accept full,
partial, and pairwise rankings, and provides methods to break down full or
partial rankings into their pairwise components. Please see Generalized
Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
some of our methods.

Estimation.RUM.Nonparametric: Nonparametric RUM Estimator

Description

Usage

Arguments

Examples