2 Probability Constraints on Linear
نویسنده
چکیده
Linear ?-minimax estimators for a bounded normal mean have been explored by Vidakovic and DasGupta (1992). They have found that the risk of linear ?-minimax rules is close to the risk of exact ?-minimax rules. However, the linear rules are not very natural when one estimates bounded parameters. In this paper we explore some modiications of linear rules with two goals in mind: (i) addressing the problem that linear rules necessarily give values outside of parameter space, and (ii) improving the risk properties of simple rules by considering polynomial rules.
منابع مشابه
Support vector regression with random output variable and probabilistic constraints
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...
متن کاملOn Distributionally Robust Chance-Constrained Linear Programs1
In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. A usual approach in this setting is to enforce the constraints up to a given level of probability. We show that, for a wide class of probability distributions (namely, radial distributions) on the data, the probability constraints can be converted explicitly into convex se...
متن کاملProduction Constraints Modelling: A Tactical Review Approach
A constraint is a limitation or a restriction that poses a threat to the performance and efficiency of a system. This paper presented a tactical review approach to production constraints modeling. It discussed the theory of constraints (TOC) as a thinking process and continuous improvement strategy to curtail constraints in other to constantly increase the performance and efficiency of a system...
متن کاملA New Method for Solving the Fully Interval Bilevel Linear Programming Problem with Equal Constraints
Most research on bilevel linear programming problem is focused on its deterministic form, in which the coefficients and decision variables in the objective functions and constraints are assumed to be crisp. In fact, due to inaccurate information, it is difficult to know exactly values of coefficients that used to construct a bilevel model. The interval set theory is suitable for describing and...
متن کاملBayesian Inference for Linear Models Subject to Linear Inequality Constraints
The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is much simpler than classical inference, but standard Bayesian computational methods become impractical when the posterior probability of the inequality constraints (under a diffuse prior) is small. ...
متن کاملOPTIMIZATION OF LINEAR OBJECTIVE FUNCTION SUBJECT TO FUZZY RELATION INEQUALITIES CONSTRAINTS WITH MAX-AVERAGE COMPOSITION
In this paper, the finitely many constraints of a fuzzy relationinequalities problem are studied and the linear objective function on the regiondefined by a fuzzy max-average operator is optimized. A new simplificationtechnique which accelerates the resolution of the problem by removing thecomponents having no effect on the solution process is given together with analgorithm and a numerical exa...
متن کامل