Maximin clusters for near-replicate regression lack of fit tests
type: Item
instance of: scholarly article
title: Maximin clusters for near-replicate regression lack of fit tests
zbMATH Open document ID: 0932.62075
author: Forrest R. Miller
author: James W. Neill
author: Brian W. Sherfey
published in: The Annals of Statistics
publication date: 1999-11-09T00:00:00Z
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F03
Mathematics Subject Classification ID: 05C90
zbMATH DE Number: 1359569
zbMATH Keywords: nonreplication
zbMATH Keywords: between clusters
zbMATH Keywords: within clusters
zbMATH Keywords: maximin power
zbMATH Keywords: lack of fit
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1214/aos/1024691249
OpenAlex ID: W1972588752
cites work: Planning experiments to detect inadequate regression models
cites work: The design of experiments for discriminating between two rival models
cites work: General Estimates of the Intrinsic Variability of Data in Nonlinear Regression Models
cites work: Lack-of-fit tests based on near or exact replicates
cites work: Q4726256
cites work: Discrimination designs for polynomial regression on compact intervals
cites work: Optimal designs for identifying the degree of a polynomial regression
cites work: Q3339960
cites work: Q4766408
cites work: Testing Departure from a Regression, without Using Replication
cites work: Nonparametric smoothing and lack-of-fit tests
cites work: Design criteria for detecting model inadequacy
cites work: A test for regression function adequacy
cites work: Testing linear regression function adequacy without replication
cites work: Selecting Check Points for Testing Lack of Fit in Response Surface Models
cites work: Testing lack of fit in regression without replication
cites work: The rainbow test for lack of fit in regression
DOI: 10.1214/AOS/1024691249
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description: scientific article; zbMATH DE number 1359569