The 2 references with contexts in paper Christopher F. Baum, Nicholas J. Cox () “Metadata for user-written contributions to the Stata programming language” / RePEc:tsj:stbull:y:2000:v:9:i:52:ip29

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Hollander, M. and D. A. Wolfe. 1973.Nonparametric Statistical Methods. New York: John Wiley & Sons.
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1. In-text reference with the coordinate start=168302
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:[0,4] ----------------------------------------------------------------Methods and Formulas The Hodges–Lehmann method is an extension of the Wilcoxon–Mann–Whitney test to the problem of estimating the shift parameter (by a median unbiased estimate and a confidence interval). The Hodges–Lehmann estimates were developed by Hodges and Lehmann (1963) and are described in detail in
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Hollander and Wolfe (1973, 75–82)
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–82), and in Lehmann (1975, 81–95). Suppose, that we have two samples of data. Sample 1 consists ofmobservations,x1;:::;xmdrawn from the distributionF(x), and sample 2 consists ofnobservations,y1;:::;yndrawn from the distributionG(y).

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Lehmann, E. L. 1975.Nonparametrics: Statistical Methods Based on Ranks. San Francisco: Holden–Day.
Total in-text references: 2
1. In-text reference with the coordinate start=165763
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Upper Saddle River, NJ: Prentice–Hall. sg123Hodges–Lehmann estimation of a shift in location between two populations Duolao Wang, London School of Hygiene and Tropical Medicine,UK, Duolao.Wang@lshtm.ac.uk Introduction The Hodges–Lehmann method (Hodges and Lehmann 1963;
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Lehmann 1975)
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is a nonparametric procedure that extends the Wilcoxon–Mann–Whitney test to the problem of estimating the shift parameter between two populations. This method gives both a point estimate and a confidence interval for the shift parameter.

2. In-text reference with the coordinate start=168344
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Methods and Formulas The Hodges–Lehmann method is an extension of the Wilcoxon–Mann–Whitney test to the problem of estimating the shift parameter (by a median unbiased estimate and a confidence interval). The Hodges–Lehmann estimates were developed by Hodges and Lehmann (1963) and are described in detail in Hollander and Wolfe (1973, 75–82), and in
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Lehmann (1975, 81–95)
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–95). Suppose, that we have two samples of data. Sample 1 consists ofmobservations,x1;:::;xmdrawn from the distributionF(x), and sample 2 consists ofnobservations,y1;:::;yndrawn from the distributionG(y).