Both the preference and the attention parameters are identified uniquely by stochastic choice data. The model is characterized by three axioms: Regularity, the acyclicity of the revealed preference relation, and a stochastic form of binariness. The model explains menu effects and stochastic ...
Mariotti. Stochastic choice and consideration sets. Econometrica, 82(3): 1153–1176, 2014. P. Manzini and M. Mariotti. State dependent choice. Social Choice and Welfare, 45(2):239– 268, 2015. P. Manzini and M. Mariotti. Dual random utility maximisation. Journal of Economic Theory, 177:...
Careful consideration needs to be given to the interpretation of the solution to such problems. References Agranov, M., Ortoleva, P.: Stochastic choice and preferences for randomization. J. Polit. Econ. 125(1), 40–68 (2017) Agranov, M., Ortoleva, P.: Ranges of Randomization. Working ...
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Your privacy, your choice We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfer...
Despite the prevalent use of SGD, it is well known that both the convergence and the performance of the algorithm are strongly dependent on the setting of appropriate values for its hyperparameters, that is all these parameters which have to be selected for running the iterative scheme, such ...
The appropriateness of the different mixture models can be discussed both biologically and in terms of statistical model choice. Within one set of genes under consideration, we assume that the same type of model (LN–LN, rLN–LN, EXP–LN) is appropriate for all genes. The parameter values, ...
As such, the choice of m should be sufficiently robust against this uncertainty inherent to the problem. We enforce robustness by using chance constraint programming, ensuring that the probability of route failure related to the choice of m does not exceed given thresholds. For this purpose, we ...
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Before proceeding to the proof of the above theorem, let us make a few comments on its statement and on the choice of[Math Processing Error]in (1.6). Notice that in any dimension[Math Processing Error], the constant[Math Processing Error]appearing in the limiting equation isstrictlypositive....