Anna Errore

Assistant Professor in the Practice of Information Systems & Operations Management

ATLANTA GA UNITED STATES

Biography

Anna Errore earned her PhD in Business Administration (area of Supply Chain and Operation, with a minor in Statistics), from Carlson School of Management, University of Minnesota, in 2021. She also holds a PhD in Industrial Engineering from the University of Palermo, Italy. Prior to joining the faculty at Emory in 2022, Anna has taught Business Statistics at the Carlson School of Management and been a graduate assistant for many related courses for undergraduates, MBAs, and Executive MBAs on subjects of Data Analytics and Operations Management. Dr. Errore’s research focuses on methodological developments for applications of advanced statistical tools for Quality and Operations Management. Specifically, her work is mostly focused on Experimental Design theory and applications. She is also an ASQ-certified Six Sigma Black Belt.

Education

University of Minnesota Carlson School of Management

PhD

Business Administration

2021

minor in Statistics

University of Palermo (Italy)

PhD

Industrial Engineering

2015

University of Palermo (Italy)

Industrial Engineering

2011

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Areas of Expertise

Experimental Design

Six Sigma

Business Statistics

Publications

Using definitive screening designs to identify active first-and second-order factor effects

Journal of Quality Technology, 49(3), 244-264

Anna Errore, Bradley Jones, William Li, Christopher J Nachtsheim

2017-11-21

Abstract
Definitive screening designs (DSDs) were recently introduced by Jones and Nachtsheim (2011b). The use of three-level factors and the desirable aliasing structure of the DSDs make them potentially suitable for identifying main effects and second-order terms in one stage of experimentation. However, as the number of active effects approaches the number of runs, the performance of standard model-selection routines will inevitably degrade. In this paper, we characterize the ability of DSDs to correctly identify first- and second-order model terms as a function of the level of sparsity, the number of factors in the design, the signal-to-noise ratio, the model type (unrestricted or following strong heredity), the model-selection technique, and the number of augmented runs. We find that minimum-run-size DSDs can be used to identify active terms with high probability as long as the number of effects is less than or equal to about half the number of runs and the signal-to-noise ratios for the active effects are above about 2.0. We also find that if minimum-run-size designs are augmented with four or more runs, the number of model terms that can be identified with high probability increases substantially. Among the model-selection methods investigated, we found that both Lasso and the Gauss–Dantzig selector (both based on AICc) can be used to effectively identify active model terms in the presence of unrestricted models. For models following strong heredity, the SHIM method developed by Choi et al. (2010) was the best among methods tested that were designed for the strong-heredity case.

Benefits and fast construction of efficient two-level foldover designs

Technometrics, 59(1) pp. 48-57

Anna Errore, Bradley Jones,William Li &Christopher J. Nachtsheim

2017-01-31

Abstract
Recent work in two-level screening experiments has demonstrated the advantages of using small foldover designs, even when such designs are not orthogonal for the estimation of main effects (MEs). In this article, we provide further support for this argument and develop a fast algorithm for constructing efficient two-level foldover (EFD) designs. We show that these designs have equal or greater efficiency for estimating the ME model versus competitive designs in the literature and that our algorithmic approach allows the fast construction of designs with many more factors and/or runs. Our compromise algorithm allows the practitioner to choose among many designs making a trade-off between efficiency of the main effect estimates and correlation of the two-factor interactions (2FIs). Using our compromise approach, practitioners can decide just how much efficiency they are willing to sacrifice to avoid confounded 2FIs as well as lowering an omnibus measure of correlation among the 2FIs.