The Estimation of Choice Probabilities from Choice Based Samples
Abstract
Ti-H CONCERN of this paper is the estimation of the parameters of a probabilistic choice model when choices rather than decision makers are sampled. Existing estimation methods presuppose an exogeneous sampling process, that is one in which a sequence of decision makers are drawn and their choice behaviors observed. In contrast, in choice based sampling processes, a sequence of chosen alternatives are drawn and the characteristics of the decision makers selecting those alternatives are observed. The problem of estimating a choice model from a choice based sample has suibstantive interest because data collection costs for such processes are often considerably smaller than for exogeneous sampling. Particular instances of this differential occur in the analysis of transportation behavior. For example, in studying choice of mode for work trips, it is often less expensive to survey transit users at the station and auto users at the parking lot than to interview commuters at their homes. Similarly, in examining choice of destination for shopping trips, surveys conducted at various shopping centers offer significant cost savings relative to home interviews.2 While interest in transportation applications provided the original motivation for our work, it has become apparent that choice based sampling processes can be cost effective in the analysis of numerous decision problems. In particular, wherever decision makers are physically clustered according to the alternatives they select,…
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Endogenous sampling can cause statistical models or algorithms to produce serious distortions.
“As a result, statistical models or algorithms risk producing serious distortions (Manski and Lerman, 1977; Manski and McFadden, 1981).”
From Forecasting Extreme Temperatures in Siberia Using Supervised Learning and Conformal Prediction Regions (2025) · cites this paper as Manski and Lerman, 1977
The design's balancing of reported and faux heat wave observations introduces endogenous sampling that over-represents unusually high temperatures compared to their presence in most real-world populations.
“However, this strategy introduces endogenous sampling that over-represents likelihood of unusually high temperatures compared to their presence in most real-world populations.”
From Forecasting Extreme Temperatures in Siberia Using Supervised Learning and Conformal Prediction Regions (2025) · cites this paper as Manski and Lerman, 1977
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Berk, R. A., & Braverman, A. (2025) · arXiv · doi:10.48550/arxiv.2503.16118
- Authors
- Manski, C. F., & Lerman, S. R.
- ORCID
- Manski, Charles F.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1977-11
- Volume
- 45
- Issue
- 8
- Pages
- 1977
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2024689521
- Cited by
- 1,208
- References
- 0
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 14 · 2023: 22 · 2022: 21 · 2021: 45 · 2020: 33 · 2019: 32 · 2018: 37 · 2017: 30 · 2016: 36 · 2015: 40 · 2014: 29 · 2013: 54 · 2012: 32
- FWCI
- 35.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Econometrica (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- choice-based sampling, stochastic choice models, estimation bias, transport behavior, mode choice, destination choice
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