Taylor & Francis
1537-2723
Quarterly
2.3
0040-1706
1959
18006347064
United States
English
YES
Google Scholar
support@tandfonline.com
Technometrics is a Journal of Statistics for the Physical, Chemical, and Engineering Sciences, and is published Quarterly by the American Society for Quality and the American Statistical Association. Since its inception in 1959, the mission of Technometrics has been to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences. The purpose of Technometrics is to contribute to the development and use of statistical and machine learning methods in physical, chemical, and engineering sciences. This vision includes developments on the interface of statistics and computer science such as data mining, machine learning, large databases, and so on. The journal places a premium on clear communication among statisticians and practitioners of these sciences and an emphasis on the application of statistical concepts and methods to problems that occur in these fields. The journal will publish papers describing new statistical techniques, papers illustrating innovative application of known statistical methods, expository papers on particular statistical methods, and papers dealing with the philosophy and problems of applying statistical methods, when such papers are consistent with the journal's objective. Every article shall include adequate justification of the application of the technique, preferably by means of an actual application to a problem in the physical, chemical or engineering sciences. All papers must contain a short, clear summary of contents and conclusions. Mathematical derivations not essential to the flow of the text should be placed in an appendix or a supplementary file. Brief descriptions of problems requiring solution and short technical notes that clearly pertain to the journal's purpose will also be considered for publication. Concise letters to the editor will be published when they are considered timely and appropriate.
The performance of reliability inference strongly depends on the modeling of the product’s lifetime distribution. Many products have complex lifetime distributions whose optimal settings a...
The performance of reliability inference strongly depends on the modeling of the product’s lifetime distribution. Many products have complex lifetime distributions whose optimal settings a...
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