ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2021
Surrogate Residuals for Discrete Choice Models.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Diagnostics for categorical response models based on quantile residuals and distance measures.Journal of applied statistics · 2025Article
- A new goodness-of-fit measure for probit models: Surrogate RThe British journal of mathematical and statistical psychology · 2023Article
- Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Discrete choice models (DCMs) are a class of models for modeling response variables that take values from a set of alternatives. Examples include logistic regression, probit regression, and multinomial logistic regression. These models are also referred together as generalized linear models. Although there exist methods for the goodness of fit of DCMs, defining intuitive residuals for such models has been difficult due to the fact that the responses are categorical values instead of continuous numbers. In this article, we propose the surrogate residual for DCMs based on the surrogate approach (Liu and Zhang 2018), which deals with an ordinal response. We consider categorical responses that may or may not be ordered. We shall show that our residual can be used to diagnose misspecification in the aspects of mean structure, individual-specific coefficients, and interaction effects. Supplementary materials for this article are available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.