2019

Technometrics Editorial Collaborators

by Apley, Daniel

Technometrics Editorial Collaborators...


SUPP: Analysis-of-Marginal-Tail-Means (ATM): A Robust Method for Discrete Black-Box Optimization

by Mak, Simon; Wu, C.F. Jeff

Supplemental material for Analysis-of-Marginal-Tail-Means (ATM): A Robust Method for Discrete Black-Box Optimization...


Analysis-of-Marginal-Tail-Means (ATM): A Robust Method for Discrete Black-Box Optimization

by Mak, Simon; Wu, C.F. Jeff

com/ loi/ utch20 Analysis- of- Marginal- Tail- Means ( ATM): A Robust Method for Discrete Black- Box Optimization Simon Mak & C. F. Jeff Wu To cite this article: Simon Mak & C. F. Jeff Wu ( 2019) Analysis- of- Marginal- Tail- Means ( ATM): A Robust Metho...


SUPP: Optimal Experimental Design in the Presence of Nested Factors

by Goos, Peter; Jones, Bradley

Supplemental material for Optimal Experimental Design in the Presence of Nested Factors...


Optimal Experimental Design in the Presence of Nested Factors

by Goos, Peter; Jones, Bradley

r 22 D- optimal 8 runs 12 runs 16 runs 8 runs 8 runs 16 runs 16 runs Machine Dial r = 2 r = 3 r = 4 ( I) ( II) 12 runs ( I) ( II) Replicates Old N. A. 4 6 8 3 2 4 5 6 New Low 2 3 4 3 3 4 6 5 New High 2 3 4 2 3 4 5 5 Diagnostics SE( .0) 0.354 0.289 0.250 ...


SUPP: Central Composite Experimental Designs for Multiple Responses With Different Models

by Marget, Wilmina M.; Morris, Max D.

Supplemental material for Central Composite Experimental Designs for Multiple Responses With Different Models...


Central Composite Experimental Designs for Multiple Responses With Different Models

by Marget, Wilmina M.; Morris, Max D.

� . 0 0 0 0 0 � . 0 0 � . 0 0 � . 0 0 0 0 0 � . 0 0 0 0 0 0 to response 1, so it is aliased with unique factor 3.. Factor 4 cannot be aliased with unique factor 2. or 3. because all three are related to response 2, nor can it be aliased with unique facto...


SUPP: Mixture of Regression Models for Large Spatial Datasets

by Kazor, Karen; Hering, Amanda S.

Supplemental material for Mixture of Regression Models for Large Spatial Datasets...


Mixture of Regression Models for Large Spatial Datasets

by Kazor, Karen; Hering, Amanda S.

org/ 10.1080/ 00401706.2019.1569558 Mixture of RegressionModels for Large Spatial Datasets Karen Kazora and Amanda S. Heringb aDepartment of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO; bDepartment of Statistical Science, Bay...


SUPP: Spatial Signal Detection Using Continuous Shrinkage Priors

by Jhuang, An-Ting; Fuentes, Montserrat; Jones, Jacob L.; Esteves, Giovanni; Fancher, Chris M.; Furman, Marschall; Reich, Brian J.

Supplemental material for Spatial Signal Detection Using Continuous Shrinkage Priors...


Spatial Signal Detection Using Continuous Shrinkage Priors

by Jhuang, An-Ting; Fuentes, Montserrat; Jones, Jacob L.; Esteves, Giovanni; Fancher, Chris M.; Furman, Marschall; Reich, Brian J.

org/ 10.1080/ 00401706.2018.1546622 Spatial Signal Detection Using Continuous Shrinkage Priors An- Ting Jhuanga, Montserrat Fuentesb, Jacob L. Jonesc, Giovanni Estevesc, Chris M. Fancherd, Marschall Furmana, and Brian J. Reicha aDepartment of Statistics,...


SUPP: Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding

by Azzimonti, Dario; Ginsbourger, David; Rohmer, Jérémy; Idier, Déborah

Supplemental material for Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding....


Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding

by Azzimonti, Dario; Ginsbourger, David; Rohmer, Jérémy; Idier, Déborah


SUPP: MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers

by Hubert, Mia; Rousseeuw, Peter J.; Van den Bossche, Wannes

Supplemental material for MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers...


MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers

by Hubert, Mia; Rousseeuw, Peter J.; Van den Bossche, Wannes

com/ loi/ utch20 MacroPCA: An All- in- One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers Mia Hubert, Peter J. Rousseeuw & Wannes Van den Bossche To cite this article: Mia Hubert, Peter J. Rousseeuw & Wannes Van den Bossc...


A Decomposition of Total Variation Depth for Understanding Functional Outliers

by Huang, Huang; Sun, Ying

Those existing methods include the functional boxplot using the modified band depth ( MBD) and 1.5 times of the 50% central region rule proposed by Sun and Genton ( 2011), outlier detection by replacing MBD with the proposed extremal depth ( ED) in the f...


SUPP: Empirical Dynamic Quantiles for Visualization of High-Dimensional Time Series

by Peña, Daniel; Tsay, Ruey S.; Zamar, Ruben

Supplemental material for Empirical Dynamic Quantiles for Visualization of High-Dimensional Time Series....



5 Whys: November 2019

by Gear, Kunita R.

Getting to know a lean & Six Sigma leader....


Ask a Belt: November 2019

by Smith, Janet

You have questions, we have experts....



Featured advertisers