A Comparison of Segment Retention Criteria for Finite Mixture Logit Models

By Rick L. Andrews, Imran S. Currim. 2003

279 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/d4a51259e1cf2fb8dfabcabd4252f7716e5c0c81

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Citations in the corpus

A review of recent approaches for capturing heterogeneity in partial least squares path modelling (2008, unknown): “), mixture logit models ( Andrews and Currim, 2003a”
Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats (2013, methodology): “In mixture models, these heuristics include model-selection criteria that are well known from the model-selection literature (e.g., AIC, BIC, and CAIC) and can also be used to approximate the best fitting number of segments (Andrews and Currim 2003a; Sarstedt et al. 2011a).”
Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (2016, background): “Results from Andrews and Currim (2003) and Dias (2004) suggest that AIC3 is a better criterion than BIC and AIC in determining the number of latent classes in LC and FM models.”
An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity (2002, background): “A study by Andrews and Currim (2003) has shown that the validation sample log-likelihood is far superior to BIC for identifying the number of components to retain for FM logit models.”
Do all countries follow the same growth process? (2009, background): “See Andrews and Currim (2003) for further discussion of this issue. schooling of the labor force in the initial year of the five-year interval.”
A Latent Class Approach to Estimating Test-Score Reliability (2011, background): “Then AIC3 ¼ 2× LLþ 3×P: A series of simulation studies for various LCMs (Andrews & Currim, 2003; Dias, 2006; Luko cien _e & Vermunt, 2010) showed that AIC tends to overestimate K, BIC tends to underestimate K, and AIC3 performed reasonably well.”
Public Preferences for Carbon Tax Attributes (2015, methodology): “(2000) and Andrews and Currim (2003), we use the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the pseudo R(2) to determine the optimal number of segments.”
Farmer preferences for milpa diversity and genetically modified maize in Mexico: a latent class approach (2007, unknown): “However, Andrews and Currim (2003) demonstrated 4 The marginalization index is commonly used to identify inequalities and design social programs in Mexico.”