Parallel algorithms for least median of squares regression

Xu, Chong-wei; Shiue, Wei-Kei

doi:10.1016/0167-9473(93)90135-g

Cited by 7 publications

(2 citation statements)

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“…Kaufman et al noted that inter-processor communication is often a critical factor and some form of load balancing is often necessary. Xu and Shiue (1993) developed three SPMD-type parallel algorithms for least median of squares (LMS) regression (Rousseeuw, 1984), using an Intel iPSC/2 MIMD computer. Parallelization strategies were deduced from consideration of the nested loop structure of sequential implementations.…”

Section: Linear Regressionmentioning

confidence: 99%

A review of parallel processing for statistical computation

et al. 1996

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Parallel computers differ from conventional serial computers in that they can, in a variety of ways, perform more than one operation at a time. Parallel processing, the application of parallel computers, has been successfully utilized in many fields of science and technology. The purpose of this paper is to review efforts to use parallel processing for statistical computing. We present some technical background, followed by a review of the literature that relates parallel computing to statistics. The review material focuses explicitly on statistical methods and applications, rather than on conventional mathematical techniques. Thus, most of the review material is drawn from statistics publications. We conclude by discussing the nature of the review material and considering some possibilities for the future.

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Section: Linear Regressionmentioning

confidence: 99%

A review of parallel processing for statistical computation

et al. 1996

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“…Several algorithms have been proposed in order to construct an exact LMS regression line in an efficient way; see, e.g., Steele and Steiger (1986), Souvaine and Steele (1987), Edelsbrunner and Souvaine (1990) and, Xu and Shiue (1993).…”

Section: Determination Of Minquantile Linesmentioning

confidence: 99%