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Θ jk = Θ jb - Θ kb

A: regression model. B: selection model. Θ jb. Θ kb. Θ jb. Θ kb. τ ². τ ². Θ jk = Θ jb - Θ kb. Θ jk = Θ jb - Θ kb. β. β 0. β 1. σ 0 ². σ ². σ 1 ². θ ijk. θ ijk. β jk. β 0jk. β 1jk. γ ijk = θ ijk + I ijk β jk √ v ijk. γ ijk = θ ijk /w ijk.

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Θ jk = Θ jb - Θ kb

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  1. A: regression model B: selection model Θjb Θkb Θjb Θkb τ² τ² Θjk=Θjb- Θkb Θjk=Θjb- Θkb β β0 β1 σ0 ² σ² σ1 ² θijk θijk βjk β0jk β1jk γijk=θijk+Iijkβjk√vijk γijk=θijk/wijk logit(wijk)=β0jk+Iijkβ1jk√vijk vijk vijk yijk yijk

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