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Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modelingNonparametric function estimationRank and other robust and distribution-free proceduresResampling methodsLack-of-fit testingMultivariate analysisInference with high-dimensional dataDimension reduction and variable selectionMethods for errors in variables, missing, censored, and other incomplete data structuresInference of stochastic processesSample surveysTime series analysisLongitudinal and functional data analysisNonparametric Bayes methods and decision proceduresSemiparametric models and proceduresStatistical methods for imaging and tomographyStatistical inverse problemsFinancial statistics and econometricsBioinformatics and comparative genomicsStatistical algorithms and machine learning.Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper. 2009 5-year Impact Factor: 0.594169; 2010 Thomson Reuters, 2009 Journal Citation Reports174;All published research articles in this journal have undergone rigorous peer review, based on initial editor screening and anonymous refereeing by independent expert referees.DisclaimerTaylor & Francis makes every effort to ensure the accuracy of all the information (the 8220;Content8221;) contained in its publications. However, Taylor & Francis and its agents and licensors make no representations or warranties whatsoever as to the accuracy, completeness or suitability for any purpose of the Content and disclaim all such representations and warranties whether express or implied to the maximum extent permitted by law. Any views expressed in this publication are the views of the authors and are not the views of Taylor & Francis.

The Journal of the American Statistical Association, published quarterly, has long been considered the premier journal of statistical science. Established in 1888, JASA focuses on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement also are reviewed.

Journal of Statistics for the Physical, Chemical, and Engineering SciencesPublished Quarterly by the American Society for Quality and the American Statistical AssociationSince 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.Technometrics publishes 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 objective. Since 1959, the methods presented in the journal have reflected changes in the discipline.In recent years, some of these changes have been associated with improved computer technology and availability, resulting in highly flexible and computer-intensive approaches to scientific data analysis. (See for example the lead article in the August 1998 issue by Richard A. Becker, Linda A. Clark, and Diane Lambert entitled "Events Defined By Duration and Severity, with an Application to Network Reliability," with discussion by Jeff Robinson and Josef Schmee.) With the emergence of massive computing and data storage capabilities, statistical approaches to screening, flexible modeling, pattern characterization, and change detection that were infeasible 20 years ago are now viable. There have also been shifts in basic attitudes about data analysis (e.g., less formal hypothesis testing, more fitted models via graphical analysis), and in how some of our important application areas are managed (e.g., quality assurance through robust design rather than detailed inspection).Many of the problems in industry today concern the analysis of huge data sets that lead to improved quality or better understanding of the manufacturing or development process. The journal would like to encourage submissions that address problems of this nature and that take advantage of modern resources.Technometrics is reviewed in Mathematical Reviews, abstracted in Applied Science & Technology Abstracts and abstracted and indexed in The Engineering Index, COMPENDEX, and Applied Science & Technology Index.