A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables
By Chandra R. Bhat. 2015
110 citations recorded by the source corpus.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/2bf789c15d2a5bfa0b984a4e5c0d8f2953805799
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In Management Science and Operations Research
- V. Shankar
- Mohamed A. Abdel-Aty
- Maria Kamargianni
- Amalia Polydoropoulou
- Fred L. Mannering
- Subodh K. Dubey
- V. Garikapati
- Felipe F. Dias
Citations in the corpus
- Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model (2015, methodology): “The aim of this paper is to empirically apply Bhat and Dubey’s (2014) formulation to develop a mode choice ICLV model that incorporates three latent psychological factors associated with safety consciousness, environmental consciousness and physical activity propensity.”
- Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis (2020, methodology): “This is generally done by using control variables such as indicator variables and fixed effects, with the intent of achieving the equivalent of a randomized trial where self-selectivity and endogeneity can be strictly eliminated (Angrist and Pischke, 2009, 2015, 2017).”
- About attitudes and perceptions: finding the proper way to consider latent variables in discrete choice models (2015, unknown): “…identification by not letting some explanatory variables impact the utility of a given alternative, both directly and through a latent variable (Bhat, 2014).2 This is indeed a sufficient but highly restrictive condition and, especially when dealing with perceptual LV, the modeller may be…”
- Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies (2017, methodology): “This finding is consistent with information previously published in the literature; for example, Bhat (20) found education to be associated with a green lifestyle, whereas Seebauer et al. (18) found a strong association between education level and technology adoption or use.”