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**1 - 3**of**3**### • Marginal Density

, 1000

"... • Observe zi ∼ N(µi, 1) for i = 1, 2,..., N • Select the m biggest ones: z(1)> z(2)> z(3)> · · ·> z(m) • Question: µ values? What can we say about their corresponding • Selection Bias selected z’s. The µ’s will usually be smaller than the ..."

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• Observe zi ∼ N(µi, 1) for i = 1, 2,..., N • Select the m biggest ones: z(1)> z(2)> z(3)> · · ·> z(m) • Question: µ values? What can we say about their corresponding • Selection Bias selected z’s. The µ’s will usually be smaller than the

### C © 2013 Biometrika Trust Printed in Great Britain

"... Highlights, trends and influences are identified associated with the pages of Biometrika subsequent to the editorship of Karl Pearson. Some key words: Biometrika; General statistical methodology; History of statistics. 1. ..."

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Highlights, trends and influences are identified associated with the pages of Biometrika subsequent to the editorship of Karl Pearson. Some key words: Biometrika; General statistical methodology; History of statistics. 1.

### dashes show the 100 largest z[i] values Frequency

, 1000

"... • Observe zi ∼ N(µi, 1) for i = 1, 2,..., N • Select the m biggest ones: z(1)> z(2)> z(3)> · · ·> z(m) • Question: µ values? What can we say about their corresponding • Selection Bias selected z’s. The µ’s will usually be smaller than the ..."

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(Show Context)
• Observe zi ∼ N(µi, 1) for i = 1, 2,..., N • Select the m biggest ones: z(1)> z(2)> z(3)> · · ·> z(m) • Question: µ values? What can we say about their corresponding • Selection Bias selected z’s. The µ’s will usually be smaller than the