On uniform design of experiments with restricted mixtures and generation of uniform distribution on some domains

Kai-Tai Fang and Zhen-Hai Yang

Statistics & Probability Letters, 2000, vol. 46, issue 2, 113-120

Abstract: In this paper we propose a new method, based on the conditional distribution method in Monte-Carlo methods, to generate the uniform distribution on the domain Tn(a,b)={(x1,...,xn): 0[less-than-or-equals, slant]ai[less-than-or-equals, slant]xi[less-than-or-equals, slant]bi[less-than-or-equals, slant]1,0[less-than-or-equals, slant]i[less-than-or-equals, slant]n, x1+...+xn=1}, where a=(a1,...,an) and b=(b1,...,bn). By this new method we can easily obtain uniform designs of experiments with mixtures, i.e., to generate a set of points that are uniformly scattered on the domain Tn(a,b). This approach can apply to generation of uniform distributions on various domains, such as convex polyhedron and simplex. These uniform distributions are useful in experimental design, reliability and optimization.

Keywords: Experimental; design; Conditional; distribution; method; Monte-Carlo; methods; Uniform; design (search for similar items in EconPapers)
Date: 2000
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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