Sparse designs for estimating variance components of nested random factors

R. A.  Bailey; Célia Fernandes; Paulo Ramos

doi:10.1016/j.jspi.2021.01.002

Sparse designs for estimating variance components of nested random factors

R. A. Bailey, Célia Fernandes, Paulo Ramos

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Abstract

A new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable size. The mean squares used in the estimation process are mutually independent, which avoids some of the problems with staggered nested designs.

Original language	English
Pages (from-to)	76-88
Journal	Journal of Statistical Planning and Inference
Volume	214
Early online date	20 Jan 2021
DOIs	https://doi.org/10.1016/j.jspi.2021.01.002
Publication status	Published - Sept 2021

Keywords

Balanced nested designs
Nested factors
Staggered nested designs
Stair nested designs
Variance components

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10.1016/j.jspi.2021.01.002

sparseMain
Copyright © 2020 Elsevier B.V. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.jspi.2021.01.002.
Accepted author manuscript, 319 KB
sparseSupplementaryAccepted author manuscript, 126 KB

Cite this

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title = "Sparse designs for estimating variance components of nested random factors",

abstract = "A new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable size. The mean squares used in the estimation process are mutually independent, which avoids some of the problems with staggered nested designs.",

keywords = "Balanced nested designs, Nested factors, Staggered nested designs, Stair nested designs, Variance components",

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AU - Bailey, R. A.

AU - Fernandes, Célia

AU - Ramos, Paulo

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N2 - A new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable size. The mean squares used in the estimation process are mutually independent, which avoids some of the problems with staggered nested designs.

AB - A new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable size. The mean squares used in the estimation process are mutually independent, which avoids some of the problems with staggered nested designs.

KW - Balanced nested designs

KW - Nested factors

KW - Staggered nested designs

KW - Stair nested designs

KW - Variance components

U2 - 10.1016/j.jspi.2021.01.002

DO - 10.1016/j.jspi.2021.01.002

M3 - Article

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VL - 214

SP - 76

EP - 88

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

ER -

Sparse designs for estimating variance components of nested random factors

Abstract

Keywords

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