musa is one of the novel techniques in csa, which lays its foundation on linear goal programming, developed for overcoming prior csa models’ weaknesses such as coping with ordinal nature of data and low fitness. employing a simple questionnaire, musa develops the interval scale and the level of satisfaction as well as determining its determinants in addition to several fruitful indices. this pa...

In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal semiparametrically with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the...

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework e...

There exists a family {Bα}α<ω1 of sets of countable ordinals such that (1) maxBα = α, (2) if α ∈ Bβ then Bα ⊆ Bβ , (3) if λ ≤ α and λ is a limit ordinal then Bα ∩ λ is not in the ideal generated by the Bβ , β < α, and by the bounded subsets of λ, (4) there is a partition {An}∞n=0 of ω1 such that for every α and every n, Bα∩An is finite.

We develop a Bayesian nonparametric framework for modeling ordinal regression relationships which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating dynamically evolving relationships between age, length and maturity, the latter recorded on an ordinal scale. The methodology builds from nonparametric mixture modeling for the joint stoch...

Several applications domains like wind forecasting in meteorology and robot control in robotics demand for learning algorithms that are able to make discrete directional predictions. We refer to this problem setting as circular ordinal regression, since it shares the same properties as traditional ordinal regression, namely the need for a specific model structure and order-preserving loss funct...

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and we...

Automatically evaluating the sentiment of reviews is becoming increasingly important due to internet growth and increasing customer and business use. We hope to address the question of what is the best model for classifying a review’s text to its labels. We propose using a classifier that combines metric labelling and ordinal regression. Our results showed that metric labeling was not improved ...