Appendix to Non-Parametric Unfolding of Binary Choice Data

1999 ◽  
Author(s):  
Keith T. Poole
Keyword(s):  
Binary Choice ◽  
Choice Data ◽  
Non Parametric

scholarly journals Simple and Weighted Unfolding Threshold Models for the Spatial Representation of Binary Choice Data

1986 ◽  
Vol 10 (3) ◽  
pp. 247-264 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Donna L. Hoffman
Keyword(s):  
Spatial Representation ◽  
Binary Choice ◽  
Threshold Models ◽  
Choice Data

scholarly journals Discrete Arrow–Pratt indexes for risk and uncertainty

2020 ◽  
Author(s):  
Aurélien Baillon ◽  
Olivier L’Haridon
Keyword(s):  
Gold Standard ◽  
Decision Models ◽  
Risk Attitudes ◽  
Choice Data ◽  
Risk And Uncertainty ◽  
Multiple Priors ◽  
Probability Transformation ◽  
Non Parametric

Abstract The Arrow–Pratt index, a gold standard in studies of risk attitudes, is not directly observable from choice data. Existing methods to measure it rely on parametric assumptions. We introduce a discrete Arrow–Pratt index, and its relative counterpart, that can be directly obtained from choices. Our approach is general: it is (i) non-parametric, (ii) applicable to both risk and uncertainty, (iii) and robust to probability transformation, non-additive beliefs and multiple priors. Our index can also be used to characterize various decision models through various simple consistency requirements. We analyze its properties and demonstrate how it can be measured.

Relative advantage maximisation as a model of context dependence for binary choice data

2014 ◽  
Vol 11 ◽  
pp. 30-42 ◽  
Author(s):  
Waiyan Leong ◽  
David A. Hensher
Keyword(s):  
Context Dependence ◽  
Relative Advantage ◽  
Binary Choice ◽  
Choice Data

Non‐parametric models in binary choice fixed effects panel data

2011 ◽  
Vol 14 (3) ◽  
pp. 351-367 ◽  
Author(s):  
Stefan Hoderlein ◽  
Enno Mammen ◽  
Kyusang Yu
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Parametric Models ◽  
Binary Choice ◽  
Non Parametric

scholarly journals An Integrated Approach to the Analysis of Binary Choice Data

1982 ◽  
Vol 6 (1) ◽  
pp. 31-40
Author(s):  
W. Cermak ◽  
J. Lieberman ◽  
Harold P. Benson
Keyword(s):  
Integrated Approach ◽  
Binary Choice ◽  
Choice Data

Unfolding Analysis of Binary Choice Data

2020 ◽  
pp. 129-180
Author(s):  
David A. Armstrong ◽  
Ryan Bakker ◽  
Royce Carroll ◽  
Christopher Hare ◽  
Keith T. Poole ◽  
...  
Keyword(s):  
Binary Choice ◽  
Choice Data ◽  
Unfolding Analysis

scholarly journals Nonparametric Unfolding of Binary Choice Data

2000 ◽  
Vol 8 (3) ◽  
pp. 211-237 ◽  
Author(s):  
Keith T. Poole
Keyword(s):  
Rank Order ◽  
Binary Choice ◽  
Choice Data ◽  
Data Set ◽  
Maximum Score ◽  
Rank Order Data ◽  
Binary Choices ◽  
Primary Focus ◽  
Linear Probability

This paper shows a general nonparametric unfolding technique for maximizing the correct classification of binary choice or two-category data. The motivation for and the primary focus of the unfolding technique are parliamentary roll call voting data. However, the procedures that implement the unfolding also can be applied to the problem of unfolding rank order data as well as analyzing a data set that would normally be the subject of a probit, logit, or linear probability analysis. One aspect of the scaling method greatly improves Manski's “maximum score estimator” technique for estimating limited dependent variable models. To unfold binary choice data two subproblems must be solved. First, given a set of chooser or legislator points, a cutting plane must be found such that it divides the legislators/choosers into two sets that reproduce the actual choices as closely as possible. Second, given a set of cutting planes for the binary choices, a point for each chooser or legislator must be found which reproduces the actual choices as closely as possible. Solutions for these two problems are shown in this paper. Monte Carlo tests of the procedure show it to be highly accurate in the presence of voting error and missing data.

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