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ΑΝΑΚΟΙΝΩΣΗ Η ΕΞΟΔΙΟΣ ΑΚΟΛΟΥΘΙΑ Θ Α ΨΑΛΕΙ ΤΗΝ ΤΡΙΤΗ 5 ΑΥΓΟΥΣΤΟΥ 2014 ΚΑΙ ΩΡΑ 16.30 ΑΠΟ ΤΟ ΝΕΚΡΟΤΑΦΕΙΟ ΚΗΦΙΣΙΑΣ. 

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1 Lecture 16. Lehmann-Scheff´ e Theorem 16.1 Definition Suppose that Y is a sufficient statistic for θ. We say that Y is complete if there are

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• Maximum likelihood
• Completeness
• Bayes estimator
• Rao–Blackwell theorem
• Loss function
• Fisher information
• Simple extension
• Statistics
• Statistical theory
• Estimation theory

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Job Search Behavior over the Business Cycle∗ Toshihiko Mukoyama Federal Reserve Board and University of Virginia [removed]

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• Unemployment
• Economics
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• Mathematics
• Logic
• Labor economics
• Matching theory
• Labour economics

Microsoft Word - +μ+ξ+ε+ι+δ+Ω+κ+σ+ε+Σ+ν+β+γ+Ω +θ+Φ+Ω+Υ+β+Σ 2 +γ+β+Χ [removed]docx

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Microsoft Word - +ν+Χ 01-14_+μ+ξ+ε+ι+δ+Ω+κ+σ+ε+Σ+ν+β+γ+Ω +θ+Φ+Ω+Υ+β+Σ +ζ+¬2.docx

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Microsoft Word - +ν+Χ 01-14_+μ+ξ+ε+ι+δ+Ω+κ+σ+ε+Σ+ν+β+γ+Ω +θ+Φ+Ω+Υ+β+Σ +ζ+¬2.docx

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1 Proof of Theorem 1 Theorem 1. Suppose K + 1 distributions pk are linearly spaced along a path γ. Assuming perfect transitions, if θ(β) and the Fisher information matrix Gθ (β) = covx∼pθ (∇θ log pθ (x)) are

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