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Orthogonal Latin hypercube designs from generalized orthogonal designs
Latin hypercube designs is a class of experimental designs that is important when computer simulations are needed to study a physical process. In this paper, we proposed some general criteria forExpand
On the construction of E(s2)-optimal supersaturated designs
Supersaturated designs are an important class of factorial designs in which the number of factors is larger than the number of runs. These designs supply an economical method to perform and analyzeExpand
On self-dual codes over some prime fields
The largest minimum weights of self-dual codes for small lengths over GF(p) where p = 11, 13, 17, 19, 23 and 29 are investigated. Expand
Some classes of orthogonal Latin hypercube designs
Latin hypercube designs (LHDs) are popularly used in designing computer experiments. A number of methods have been proposed to construct LHDs with orthogonality among the main-effects. In this paper,Expand
Supersaturated designs: A review of their construction and analysis
Supersaturated designs are fractional factorial designs in which the run size (n) is too small to estimate all the main effects. Under the effect sparsity assumption, the use of supersaturated designExpand
MDS Self-Dual Codes over Large Prime Fields
Combinatorial designs have been used widely in the construction of self-dual codes. Recently a new method of constructing self-dual codes was established using orthogonal designs. This method has ledExpand
Multi-level k-circulant Supersaturated Designs
In this paper we present a new method for constructing multi-level supersaturated designs with n rows, m columns and the equal occurrence property. We investigate the existence of multi-levelExpand
An empirical investigation of the moderating effects of BPR and TQM on ICT business value
Both BPR and TQM have considerable positive moderating effects of a similar magnitude on the relationship between ICT investment and firm value added. Expand
Modelling by supersaturated designs
  • S. Georgiou
  • Mathematics, Computer Science
  • Comput. Stat. Data Anal.
  • 1 December 2008
The singular value decomposition (SVD), principal components analysis and regression analysis are used together in an SVD principal regression method to reveal the hidden true linear model. Expand
Survival estimation through the cumulative hazard function with monotone natural cubic splines
This paper investigates the fit of a natural cubic spline on the cumulative hazard function under appropriate constraints and explores the estimation of survival probabilities via a smoothed version of the survival function, in the presence of censoring. Expand