Results 41 to 50 of about 8,539 (159)

Estimating Multinomial Logit Models with Samples of Alternatives [PDF]

Sociological Methodology, 2018
This comment reconsiders advice offered by Bruch and Mare regarding sampling choice sets in conditional logistic regression models of residential mobility. Contradicting Bruch and Mare’s advice, past econometric research shows that no statistical correction is needed when using simple random sampling of unchosen alternatives to pare down respondents ...
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Comparing Generalized Multinomial Logit Model and Random Parameter Logit Model in Preference Space and in Willingness-to-Pay Space

Journal of Agriculture and Food Research
The Generalized Multinomial Logit (GMNL) model and the Random Parameter Logit (RPL) model are two specifications of the Mixed Multinomial Logit (MMNL) model. These models can be estimated in preference space or in Willingness-to-Pay (WTP) space.
Xianwen Chen
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Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Applied Sciences, 2021
The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes.
Fulu Wei, Zhenggan Cai, Zhenyu Wang, Yongqing Guo, Xin Li, Xiaoyan Wu   +5 more
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Comparing methods for risk prediction of multicategory outcomes: dichotomized logistic regression vs. multinomial logit regression

BMC Medical Research Methodology
Background Medical outcomes of interest to clinicians may have multiple categories. Researchers face several options for risk prediction of such outcomes, including dichotomized logistic regression and multinomial logit regression modeling.
Lei Li, Matthew A. Rysavy, Georgiy Bobashev, Abhik Das   +3 more
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Development of parameters of multinomial logit models

Mathematical and Computer Modelling, 1988
The use of multinomial logit models for predicting mode-choice decisions for urban travel has gained prominence in recent years. The prime advantage of these models lies in their ability to allocate travel demand among many modes at the same time, as opposed to the traditional binary mode-choice models developed in the early sixties. A major impediment
Khasnabis, S., Cynecki, M.J.
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Comparing the efficiency and robustness of state-of-the-art experimental designs for stated choice modeling: A simulation analysis

Advances in Mechanical Engineering, 2017
Among the ways to construct experimental designs having been proposed, orthogonal design, uniform design, and D-efficient design are state-of-the-art methods. This article provides detailed comparisons on the efficiency and robustness among these methods
Hai Zhu, Xia Luo, Yanjin Li, Ying Zhu, Qian Huang   +4 more
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Generations, permanent income and housing tenure choice: A multinomial logit model approach [PDF]

Decision Science Letters
This paper examines how generational cohorts influence households’ choices regarding housing tenure and considers the diverse preferences and socio-economic factors that shape decisions—using survey data on 425 families in Ho Chi Minh City ...
Thuy Tien Huynh, Dang Thuy Truong
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Estimation of a constrained multinomial logit model

Transportation, 2012
Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous ...
Castro, Marisol, Martínez Concha, Francisco, Munizaga Muñoz, Marcela   +2 more
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The crowding-out effect of sugar-sweetened beverages (SSBs) on household expenditure patterns in Bangladesh

BMC Public Health, 2023
Background Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities.
Abul Kalam Azad, Rumana Huque
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Comparing discrete choice and machine learning models in predicting destination choice

European Transport Research Review
Destination choice modeling has long been dominated by theory-based discrete choice models. Simultaneously, machine learning has demonstrated improved predictive performance to other fields of discrete choice modeling.
Ilona Rahnasto, Martijn Hollestelle
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