Suboptimal Measures of Predictive Complexity for Absolute Loss Function

On Predictive Density Estimation for Location Families under Integrated Absolute Error Loss

Tatsuya Kubokawa, Éric Marchand, William E. Strawderman a Department of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, JAPAN (e-mail: [email protected]) b Université de Sherbrooke, Département de mathématiques, Sherbrooke Qc, CANADA, J1K 2R1 (e-mail: [email protected]) c Rutgers University, Department of Statistics and Biostatistics, 501 Hill Center, Bu...

Suboptimal model predictive control (feasibility implies stability)

Practical difficulties involved in implementing stabilizing model predictive control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In this paper, we first establish conditions under which suboptimal model pr...

Two Absolute Bounds for Distributed Bit Complexity

The concept of distributed communication bit complexity was introduced by Dinitz, Rajsbaum, and Moran. They studied bit complexity of Consensus and Leader Election, arriving at more or less exact bounds. This paper answers two questions on Leader Election, which remained there open. The first is to close the gap between the known upper and lower bounds, for electing a leader by two linked proce...

Quality Loss Function – A Common Methodology for Three Cases

The quality loss function developed by Genichi Taguchi considers three cases, nominal-thebest, smaller-the-better, and larger-the-better. The methodology used to deal with the larger-thebetter case is slightly different than the other two cases. This research employs a term called target-mean ratio to propose a common formula for all three cases to bring about similarity among them. The target-...

"Absolute" or "added" predictive value?