1,721,253 research outputs found
Optimal design generation: an approach based on discovery probability
Full text linkEfficient algorithms for searching for optimal saturated designs for sampling experiments are widely available. They maximize a given efficiency measure (such as D-optimality) and provide an optimum design. Nevertheless, they do not guarantee a global optimal design. Indeed, they start from an initial random design and find a local optimal design. If the initial design is changed the optimum found will, in general, be different. A natural question arises. Should we stop at the design found or should we run the algorithm again in search of a better design? This paper uses very recent methods and software for discovery probability to support the decision to continue or stop the sampling. A software tool written in SAS has been developed
Competing technologies and market dominance: standard 'battles' in the Local Area Networking industry
Full text linkNo abstract availabl
Computational aspects in orthogonal fractional factorial design generation
No full textGeneration of orthogonal fractional factorial designs (OFFDs) is an important and extensively studied subject in applied statistics. In this paper we analyse computational methods that originate by the joint use of polynomial counting functions and algebraic strat
Statistical methods for tourism data: an application to the regional information system of Piemonte (Italy)
No full textRandom Latin squares and Sudoku designs generation
No full textUniform random generation of Latin squares is a classical problem. In this paper we prove that both Latin squares and Sudoku designs are maximum cliques of properly defined graphs. We have developed a simple algorithm for uniform random sampling of Latin squares and Sudoku
designs. The corresponding SAS code is available in the supplementary material
Fractional factorial design for model based evaluation of customer preferences
No full textIn this article, we explore the connection between Conjoint Analysis (CA) and a recent theory for minimum size orthogonal fractional factorial design generation (Fontana, 2013). We show how searching for a minimum size OFFD that satisfies a set of constraints, expressed in terms of orthogonality between simple and interaction effects, is equivalent to solving an integer linear programming problem. It is worth noting that the methodology puts no restriction either on the number of levels of each factor or on the orthogonality constraints and so it can be applied to a very wide range of designs, including mixed orthogonal arrays. An algorithm, that has been implemented in SAS/IML, is briefly described. The use of thi
Algebraic generation of minimum size orthogonal fractional factorial designs: an approach based on integer linear programming
Full text linkGeneration of orthogonal fractional factorial designs (OFFDs) is an important and extensively studied subject in applied statistics. In this paper we show how searching for an OFFD that satisfies a set of constraints, expressed in terms of orthogonality between simple and interaction effects, is, in many applications, equivalent to solving an integer linear programming problem.We use a recent methodology, based on polynomial counting functions and strata, that represents OFFDs as the positive integer solutions of a system of linear equations. We use this system to set up an optimization problem where the cost function to be minimized is the size of the OFFD and the constraints are represented by the system itself. Finally we search for a solution using standard integer programming techniques. Some applications are also presented in the computational results section. It is worth noting that the methodology does not put any restriction either on the number of levels of each factor or on the orthogonality constraints and so it can be applied to a very wide range of designs, including mixed orthogonal array
- …
