A separability consistency condition on choice probabilities in composite menus, combined with monotonicity and continuity, fully characterizes the random coefficients logit family.
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A composite-likelihood EM algorithm with importance sampling yields computationally feasible, asymptotically valid inference for the Poisson log-normal model on moderately large multivariate count datasets.
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A Separability Foundation for Random Coefficients Logit
A separability consistency condition on choice probabilities in composite menus, combined with monotonicity and continuity, fully characterizes the random coefficients logit family.
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Composite likelihood inference for the Poisson log-normal model
A composite-likelihood EM algorithm with importance sampling yields computationally feasible, asymptotically valid inference for the Poisson log-normal model on moderately large multivariate count datasets.