Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.

Use of the logit scaling approach to test for rank-order and fatigue effects in stated preference data

Abstract: The scaling approach is a statistical estimation method which allows for differences in the amount of unexplained variation in different types of data which can then be used together in analysis. In recent years, this approach has been tested and recommended in the context of combining Stated Preference and Revealed Preference data. The paper provides a description of the approach and a historical overview. The scaling approach can also be used to identify systematic differences in the variance of choices within a single Stated Preference data set due to the way in which the hypothetical choice situations are presented or the responses are obtained. The paper presents the results of two case studies — one looking at rank order effect and the other at fatigue effect. Scale effects appear to exist in both cases: the amount of unexplained variance is shown to increase as rankings become lower, and as the number of pairwise choices completed becomes greater. The implications of these findings for the use of SP ranking tasks and repeated pairwise choice tasks are discussed.

Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement.

Abstract: Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences. Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons. As an alternative, we propose to explicitly model pairwise word interactions and present a novel similarity focus mechanism to identify important correspondences for better similarity measurement. Our ideas are implemented in a novel neural network architecture that demonstrates state-ofthe-art accuracy on three SemEval tasks and two answer selection tasks.

The estimation of pairwise relationships.

Abstract: Relationships between the individuals of a population have been previously studied from the point of view of prediction. Edwards (1967) suggested that the problem of detailed population structure could also be studied from the point of view of inference. Even where inferences of practical applicability cannot be made, such an approach can increase understanding of the relation between genealogical and genetic structure. In this paper we consider a specific problem which provides an introduction to the ideas and methods of genealogical inference. This is the problem of estimating the pairwise relationship between two individuals on the basis of their phenotypes at several loci. There is no theoretical problem in the extension from pairwise to joint relationship.

A Microeconometric Test of Alternative Stochastic Theories of Risky Choice

Abstract: The random preference, Fechner (or ‘white noise’), and constant error (or ‘tremble’) models of stochastic choice under risk are compared. Various combinations of these approaches are used with expected utility and rank-dependent theory. The resulting models are estimated in a random effects framework using experimental data from two samples of 46 subjects who each faced 90 pairwise choice problems. The best fitting model uses the random preference approach with a tremble mechanism, in conjunction with rank-dependent theory. As subjects gain experience, trembles become less frequent and there is less deviation from behaviour consistent with expected utility theory.