Aim & Scope
The mission of Technometrics is to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences. Its content features papers that describe new statistical techniques, illustrate innovative application of known statistical methods, or review methods, issues, or philosophy in a particular area of statistics or science, when such papers are consistent with the journal's mission. Application of proposed methodology is justified, usually by means of an actual problem in the physical, chemical, or engineering sciences. Papers in the journal reflect modern practice. This includes an emphasis on new statistical approaches to screening, modeling, pattern characterization, and change detection that take advantage of massive computing capabilities. Papers also reflect shifts in attitudes about data analysis (e.g., less formal hypothesis testing, more fitted models via graphical analysis), and in how important application areas are managed (e.g., quality assurance through robust design rather than detailed inspection). [1]
2024
Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data
J Oguamalam , U Radojičić , P Filzmoser
Technometrics , 2024 , pp 1-12.
Linear Source Apportionment using Generalized Least Squares
Technometrics , 2024 , pp 1-16.
Drift vs Shift: Decoupling Trends and Changepoint Analysis
H Wu , T Schafer , S Ryan , D Matteson
Technometrics , 2024 , pp 1-16.
Screening designs for continuous and categorical factors
B Jones , R Lekivetz , D Majumdar , C Nachtsheim
Technometrics , 2024 , pp 1-16.
On tracking varying bounds when forecasting bounded time series
Technometrics , 2024 , pp 1-16.
Constrained Bayesian Optimization with Lower Confidence Bound
Technometrics , 2024 , pp 1-19.
Image Comparison Based On Local Pixel Clustering
No authors listed.
Technometrics , 2024 , pp 1-19.
Moving sum procedure for change point detection under piecewise linearity
No authors listed.
Technometrics , 2024 , pp 1-19.