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http://dx.doi.org/10.5351/KJAS.2011.24.1.137

Order-Restricted Inference with Linear Rank Statistics in Microarray Data  

Kang, Moon-Su (National Cancer Center Research Institute)
Publication Information
The Korean Journal of Applied Statistics / v.24, no.1, 2011 , pp. 137-143 More about this Journal
Abstract
The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear rank statistics along with that principle, yield a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in a small sample. Applications of this method are illustrated in a real microarray data example (Lobenhofer et al., 2002).
Keywords
Microarray; union-intersection principle; linear rank statistics; permutation;
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