Nonparametric empirical Bayes growth curve analysis

N. S. Altman; G. Casella

doi:10.1080/01621459.1995.10476542

Nonparametric empirical Bayes growth curve analysis

N. S. Altman, G. Casella

Statistics

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

Original language	English (US)
Pages (from-to)	508-515
Number of pages	8
Journal	Journal of the American Statistical Association
Volume	90
Issue number	430
DOIs	https://doi.org/10.1080/01621459.1995.10476542
State	Published - Jun 1995

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1080/01621459.1995.10476542

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title = "Nonparametric empirical Bayes growth curve analysis",

abstract = "Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.",

author = "Altman, {N. S.} and G. Casella",

note = "Funding Information: * The authors were supported by National Science Foundation Grant DMS89-16245. N. S. Altman also received support from Hatch Grant 15 140 NYF, and G. Casella also received support from National Science Foundation Grant DMS9 100829 and National Science Academy Grant 90F-073.",

year = "1995",

month = jun,

doi = "10.1080/01621459.1995.10476542",

language = "English (US)",

volume = "90",

pages = "508--515",

journal = "Journal of the American Statistical Association",

issn = "0162-1459",

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T1 - Nonparametric empirical Bayes growth curve analysis

AU - Altman, N. S.

AU - Casella, G.

N1 - Funding Information: * The authors were supported by National Science Foundation Grant DMS89-16245. N. S. Altman also received support from Hatch Grant 15 140 NYF, and G. Casella also received support from National Science Foundation Grant DMS9 100829 and National Science Academy Grant 90F-073.

PY - 1995/6

Y1 - 1995/6

N2 - Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

AB - Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

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DO - 10.1080/01621459.1995.10476542

M3 - Article

AN - SCOPUS:21844485634

SN - 0162-1459

VL - 90

SP - 508

EP - 515

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 430

ER -

Nonparametric empirical Bayes growth curve analysis

Abstract

All Science Journal Classification (ASJC) codes

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