مقایسهٔ روش‌ها

	مدل لاجیت ترکیبی (Mixed Logit Model)	رگرسیون لجستیک چندجمله‌ای (Multinomial Logistic Regression)	مدل‌های برهم‌کنش فضایی (گرانشی)
حوزه	اقتصادسنجی	اقتصادسنجی	تحلیل فضایی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	2000	1974	1971
پدیدآور	Daniel McFadden & Kenneth Train	McFadden	Alan Wilson (entropy-maximizing family)
نوع	Random-parameters discrete choice model	Multinomial logistic regression	Model of flows between spatial origins and destinations
منبع بنیادین	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.
نام‌های دیگر	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli
مرتبط	3	5	4
خلاصه	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls with separation, and Wilson's 1971 entropy-maximizing family put these models on a rigorous footing for transport, migration, and retail analysis.