What can lattices do for experimental designs?

Let x 1:=((x 1)iϵI)ϵ R′ be a finite data set weighted by n 1:=((n 1)iϵI)ϵ R′ (reflecting a previous derivation or is constant), F: = {A,B,…} be a set of factors which index partitions of 1 (denoted by A, B…) together with ‘experimental dimensions’ (subjects' ages…), the experimental design be D...

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Published inMathematical social sciences Vol. 11; no. 3; pp. 243 - 281
Main Author Duquenne, V.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.1986
Elsevier
SeriesMathematical Social Sciences
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Abstract Let x 1:=((x 1)iϵI)ϵ R′ be a finite data set weighted by n 1:=((n 1)iϵI)ϵ R′ (reflecting a previous derivation or is constant), F: = {A,B,…} be a set of factors which index partitions of 1 (denoted by A, B…) together with ‘experimental dimensions’ (subjects' ages…), the experimental design be D F: = Im(I → X FF), ( P 1,⩽,∨,∧,I,1) and (Q ⊥,⩽,∨∨⊥, 0.1) denote the lattices of partition on I and of ortho-projections in R 1 〈x 1 | y 1〉: = Σ 1ϵ1n 1x 1y 1. F := {Fϵ P/ all Fϵ F∼ ; the effect ascribed to Aϵ F is evaluated through mA(x 1: (〈 x ̄ u〉 1) ja where each a is the A-class containing i, ( x a) 1: = 1 n a ∑ jϵa n jx 1, n a: = ∑ jϵa n. Let cA: = mA1 − m1 ϵQ ⊥, the map c: F · Q ⊥, A →cA established a connection P 1 = ⇆ [0,cI] which makes precise the link between the set theoretical description of an experiment and its analysis linear framework. A, B ϵ P 1 are said loccally orthogonal (LO) iff n a≥ b = n a n b , n u (all u ϵ A ∨ B, all a, b, ⊆ u); then the relative interaction ca · cB: = cA ∨ B acA ∧ cB) (called interaction, denoted by cA · cB whenever A ∧ B = 1 is a sum of partial interactions: cA · cB⊕⊥ uϵ A ∧ cA/ u · cB/ u with A/ u: = { a ϵ A/ a ⊆ u}. Any (pairwise) LO-subset A ⊆ P 1 induces (by c) a commutative Boolean subalgebra Q( X) ⊆ Q 1, whose atoms give the c∨ X canonical ⊕⊥- decomposition according to X. Let F generate an LO-design iff the generated sublattice (by ∨ and V) P( F) ⊆ P 1 is LO; using the Möbius function of if P ( F ), ⩾), the 9I canonical ⊕⊥-decomposition is easily calculated and labelled with formulae: its terms are expressible as n-ary(relative) interactions between ·-irreducible terms associated with the projective intervals of P( F) . A concrete example from psychology is discussed.
AbstractList Let x 1:=((x 1)iϵI)ϵ R′ be a finite data set weighted by n 1:=((n 1)iϵI)ϵ R′ (reflecting a previous derivation or is constant), F: = {A,B,…} be a set of factors which index partitions of 1 (denoted by A, B…) together with ‘experimental dimensions’ (subjects' ages…), the experimental design be D F: = Im(I → X FF), ( P 1,⩽,∨,∧,I,1) and (Q ⊥,⩽,∨∨⊥, 0.1) denote the lattices of partition on I and of ortho-projections in R 1 〈x 1 | y 1〉: = Σ 1ϵ1n 1x 1y 1. F := {Fϵ P/ all Fϵ F∼ ; the effect ascribed to Aϵ F is evaluated through mA(x 1: (〈 x ̄ u〉 1) ja where each a is the A-class containing i, ( x a) 1: = 1 n a ∑ jϵa n jx 1, n a: = ∑ jϵa n. Let cA: = mA1 − m1 ϵQ ⊥, the map c: F · Q ⊥, A →cA established a connection P 1 = ⇆ [0,cI] which makes precise the link between the set theoretical description of an experiment and its analysis linear framework. A, B ϵ P 1 are said loccally orthogonal (LO) iff n a≥ b = n a n b , n u (all u ϵ A ∨ B, all a, b, ⊆ u); then the relative interaction ca · cB: = cA ∨ B acA ∧ cB) (called interaction, denoted by cA · cB whenever A ∧ B = 1 is a sum of partial interactions: cA · cB⊕⊥ uϵ A ∧ cA/ u · cB/ u with A/ u: = { a ϵ A/ a ⊆ u}. Any (pairwise) LO-subset A ⊆ P 1 induces (by c) a commutative Boolean subalgebra Q( X) ⊆ Q 1, whose atoms give the c∨ X canonical ⊕⊥- decomposition according to X. Let F generate an LO-design iff the generated sublattice (by ∨ and V) P( F) ⊆ P 1 is LO; using the Möbius function of if P ( F ), ⩾), the 9I canonical ⊕⊥-decomposition is easily calculated and labelled with formulae: its terms are expressible as n-ary(relative) interactions between ·-irreducible terms associated with the projective intervals of P( F) . A concrete example from psychology is discussed.
Author Duquenne, V.
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Cites_doi 10.32917/hmj/1557281060
10.1007/BF00531932
10.1215/S0012-7094-42-00942-6
10.1016/S0021-9800(66)80009-1
10.32917/hmj/1557367273
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Issue 3
Keywords canonical decomposition
permutable partitions
modular lattices
analysis of variance
projective interval
Experimental design
Möbius function
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Snippet Let x 1:=((x 1)iϵI)ϵ R′ be a finite data set weighted by n 1:=((n 1)iϵI)ϵ R′ (reflecting a previous derivation or is constant), F: = {A,B,…} be a set of...
SourceID repec
crossref
elsevier
SourceType Index Database
Enrichment Source
Publisher
StartPage 243
SubjectTerms analysis of variance
canonical decomposition
direct meet representation
Experimental design
formulae for canonical decompositions
modular lattices
Möbius function
permutable partitions
projective interval
Title What can lattices do for experimental designs?
URI https://dx.doi.org/10.1016/0165-4896(86)90028-4
http://econpapers.repec.org/article/eeematsoc/v_3a11_3ay_3a1986_3ai_3a3_3ap_3a243-281.htm
Volume 11
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