In this paper we propose new estimation techniques in connection with regression models whose errors have distributions which are members of the celebrated Pearson’s system. Efficient MCMC procedures are proposed in the context of likelihood—based inference. The new techniques are applied to...

In this paper we derive both primal and dual-cost systems in which the stochastic specifications arise from the model (random environment or measurement errors and optimization errors)—not tacked on at the end after the deterministic system is worked out. Derivation of the error structures is...