Rank estimates in a class of semiparametric two-sample models |
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| Authors: | Dorota M Dabrowska Kjell A Doksum Ryozo Miura |
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| Institution: | (1) Department of Statistics, University of California, 94720 Berkeley, CA, U.S.A.;(2) Osaka City University, 558 Sumiyoshi-ku, Osaka, Japan;(3) Present address: Department of Statistics, Harvard University, 02138 Cambridge, MA, U.S.A. |
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| Abstract: | We consider a two-sample semiparametric model involving a real parameter  and a nuisance parameter F which is a distribution function. This model includes the proportional hazard, proportional odds, linear transformation and Harrington-Fleming models (1982, Biometrika, 69, 533–546). We propose two types of estimates based on ranks. The first is a rank approximation to Huber's M-estimates (1981, Robust Statistics, Wiley) and the second is a Hodges-Lehmann type rank inversion estimate (1963, Ann. Math. Statist., 34, 598–611). We obtain asymptotic normality and efficiency results. The estimates are consistent and asymptotically normal generally but fully efficient only for special cases.Research partially supported by National Science Foundation Grant DMS-86-02083 and National Institute of General Medical Sciences Grant SSS-Y1RO1GM35416-01 |
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| Keywords: | Semiparametric transformation models M-estimates based on ranks Hodges-Lehmann estimates |
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