Comparing search and estimation performances of designs based on a compound criterion

Talebi, Hooshang; Esmailzadeh, Nabaz

doi:10.1080/02331888.2014.990018

Cited by 3 publications

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“…To be more precise, we also used the compound criterion given in Talebi and Esmailzadeh (2015) to calculate the estimation and discrimination performances of the rival designs, simultaneously. The criteria are plotted against m in Figure 4.…”

Section: Discussionmentioning

confidence: 99%

Small run size design for model identification in 3^m factorial experiments

Labbaf

Talebi

2020

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An active interaction in a main effect plan may cause biased estimation of the parameters in an analysis of variance (ANOVA) model. A fractional factorial design (FFD) with higher order resolution can resolve the alias problem, however, with a considerable number of runs. Alternatively, a search design (SD), the so‐called main effect plus k plan (MEP.k), with much less number of runs than FFD, is able to search for k possible active interactions and estimate them in addition to estimating the main effects. However, the existing MEP.k's for 3m factorial experiments are either proposed for a large m (e.g. m≥13) or have a large number of runs. In this paper, we proposed an irregular design for 3m factorial experiments, which is able to identify the active two‐factor interactions and estimate them along with estimating the general mean and main effects for 3≤m≤14. The obtained design has fewer runs than the previous designs; meanwhile, it is also comparable and competitive in the discrimination and estimation performances with them. By simulation studies, it is shown that the proposed design does well in model identification and variable selection.

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Section: Discussionmentioning

confidence: 99%

Small run size design for model identification in 3^m factorial experiments

Labbaf

Talebi

2020

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“…Therefore, a comprehensive performance evaluation of design for multitask purposes is desirable. Several researchers have studied such problems and suggested dual‐task design criteria (see Biswas & Chaudhuri, 2002; Hill et al, 1968; McGree & Eccleston, 2008 and; Talebi & Esmailzadeh, 2015). We advance the methodology for efficient designing a factorial experiment by accounting for the missing observations.…”

Section: Dual‐task Design Criterionmentioning

confidence: 99%

Design criteria for model discrimination in factorial experiments with potential failing runs

Motavaze

Talebi

2023

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In this study, we considered the problem of design for model discrimination in factorial experiments, while there is potential to miss runs. Under such a situation, we questioned the reliability of the superior selected design based on the existing design criteria for model discrimination. In other words, any selected design may miss its efficiency in discriminating between models in the presence of failing runs. To this end, we assumed the missing probability of the runs based on some available information parameters and developed a new design criterion for model discrimination. The proposed Bayesian criterion makes allowance for counting the missing probabilities of runs. The Bayesian framework also takes into account the intercorrelation between the factorial effects. The numerical examples demonstrate the superiority of the obtained criterion to the existing ones in differentiating the rival design for model discrimination. We also considered the problem of design robustness to missing observations and suggested a ratio criterion for selecting the robust design. It was found that a robust design is not necessarily the most efficient in model discrimination and vice versa. Moreover, we also pointed to the estimation efficiency of the designs and gave a compound dual‐task design criterion.

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Search Probability for Non-zero Effects Detection under Skew-Normal/Independent Search Model

Sadeghi

Talebi

2018

JSRI

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Comparing search and estimation performances of designs based on a compound criterion

Cited by 3 publications

References 15 publications

Small run size design for model identification in 3^m factorial experiments

Small run size design for model identification in 3^m factorial experiments

Design criteria for model discrimination in factorial experiments with potential failing runs

Search Probability for Non-zero Effects Detection under Skew-Normal/Independent Search Model

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Comparing search and estimation performances of designs based on a compound criterion

Cited by 3 publications

References 15 publications

Small run size design for model identification in 3m factorial experiments

Small run size design for model identification in 3m factorial experiments

Design criteria for model discrimination in factorial experiments with potential failing runs

Search Probability for Non-zero Effects Detection under Skew-Normal/Independent Search Model

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Product

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Small run size design for model identification in 3^m factorial experiments

Small run size design for model identification in 3^m factorial experiments