Books on the topic 'Interval data'

Meisen, Philipp. Analyzing Time Interval Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15728-9.

Engineers, National Association of Corrosion. Standard format for computerized close interval survey data. Houston: NACE, 1992.

Pękala, Barbara. Uncertainty Data in Interval-Valued Fuzzy Set Theory. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-93910-0.

Interval-censored time-to-event data: Methods and applications. Boca Raton: Chapman and Hall/CRC, 2012.

Krämer, Walter. Scientific Computing, Validated Numerics, Interval Methods. Boston, MA: Springer US, 2001.

Servin, Christian, and Vladik Kreinovich. Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12628-9.

Kasperski, Adam. Discrete optimization with interval data: Minmax regret and fuzzy approach. Berlin: Springer, 2008.

Rheinfurth, M. Weibull distribution based on maximum likelihood with interval inspection data. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1985.

Kreinovich, Vladik, Anatoly Lakeyev, Jiří Rohn, and Patrick Kahl. Computational Complexity and Feasibility of Data Processing and Interval Computations. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2793-7.

Kasperski, Adam. Discrete optimization with interval data: Minmax regret and fuzzy approach. Berlin: Springer, 2008.

Kasperski, Adam. Discrete optimization with interval data: Minmax regret and fuzzy approach. Berlin: Springer, 2008.

Lee, Jong-Tae. Comparison of methods of point and interval estimation using Workers' Compensation Board data. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.

Kline, Patrick. Interval estimation of potentially misspecified quantile models in the presence of missing data. Cambridge, MA: National Bureau of Economic Research, 2010.

Balce, A. E. Determination of optimum sampling interval in DEM data acquisition: A guide for users. Edmonton: Alberta Forestry, 1986.

Kosheleva, Olga, Sergey P. Shary, Gang Xiang, and Roman Zapatrin, eds. Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31041-7.

Kim, Hakil. A method of classification for multisource data in remote sensing based on interval-valued probabilities. West Lafayette, Ind: Laboratory for Applications of Remote Sensing, Purdue University, 1990.

1941-, Rokne J., ed. New computer methods for global optimization. Chichester, West Sussex, England: Horwood, 1988.

Kim, Hakil. A method of classification for multisource data in remote sensing based on interval-valued probabilties. West Lafayette, Indiana: Laboratory for Applications of Remote Sensing and School of Electrical Engineering, Purdue University, 1990.

Commission, Victoria Essential Services. Final decision: Energy retail code, technical amendments, bulk hot water and bills based on interval meter data. Melbourne, VIC: Essential Services Commission, 2005.

Lee, Hyeon-Soo. Approximate interval estimation methods for the reliability of systems using component data with exponential and Weibull distributions. Monterey, Calif: Naval Postgraduate School, 1989.

J, Smith Richard. Measurement error with accounting constraints: Point and interval estimation for latent data with an application toUK gross domestic product. Cambridge: Department of Applied Economics, University of Cambridge, 1995.

Ashchepkov, L. T. Universalʹnye reshenii︠a︡ intervalʹnykh zadach optimizat︠s︡ii i upravlenii︠a︡. Moskva: Nauka, 2006.

Nguyen, Hung T. Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2012.

Date, C. J. Temporal data and the relational model: A detailed investigation into the application of interval and relation theory to the problem of temporal database management. San Diego, CA: Morgan Kaufmann Publishers, 2003.

Inman, Ernest J. Comparison of the 2-, 25-, and 100-year recurrence interval floods computed from observed data with the 1995 urban flood-frequency estimating equations for Georgia. Atlanta, Ga: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

John, Fry. Disease data book. Lancaster: MTP Press, 1986.

1928-, Sandler Gerald, and Brooks David 1939-, eds. Disease data book. Lancaster: MTP Press, 1986.

1928-, Sandler Gerald, and Brooks David 1939-, eds. Disease data book. Lancaster, England: MTP Press, 1986.

Unit, Engineering Sciences Data. Engineering sciences data: fluid mechanics, internal flow. London: Engineering Sciences Data Unit, 1986.

Unit, Engineering Sciences Data. Engineering sciences data: fluid mechanics, internal flow. London: Engineering Sciences Data Unit, 1988.

Coderre, David G. Internal Audit. New York: John Wiley & Sons, Ltd., 2009.

Alzheimer, Jim. A data warehouse at the Internal Revenue Service. [Atlanta, Ga.]: Information Management Forum, 1998.

Parsons, Graham. Inventory of internal trade data and expertise: Report to the Internal Trade Secretariat. Winnipeg, Man: The Secretariat, 1997.

Collinson, Mark. Migration and changing settlement patterns: Multilevel data for policy. Pretoria: Statistics South Africa, 2006.

Kenah, Lawrence J. VAX/VMS internals and data structures: Version4.4. Bedford, Mass: Digital Press, 1988.

Blumenthal, Martin Benno. Interpretation of equatorial current meter data as internal waves. Woods Hole, Mass: Woods Hole Oceanographic Institution [1987], 1987.

Chambers, Andrew D. Effective internal audits: How to plan and implement. London: Financial Times, 1992.

Chambers, Andrew D. Effective internal audits: How to plan and implement. Spilsby, Lincs: Management Audit, 1997.

Office, General Accounting. IRS-Customs data systems exchange. Washington, D.C: The Office, 1993.

Saro, Saravanan, ed. OpenVMS AXP internals and data structures: Version 1.5. Salem, MA: Digital Press, 1994.

Kenah, Lawrence J. VAX/VMS Internals and Data Structures: Version 4.4. Bedford, Mass: Digital Press, 1988.

Goldenberg, Ruth. VMS for alpha platforms, internals and data structures. Hemel Hempstead: Prentice-Hall, 1992.

Goldenberg, Ruth E. VAX/VMS internals and data structures: Version 5.2. [Bedford, MA]: Digital Press, 1991.

Discrete Optimization with Interval Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78484-5.

Meisen, Philipp. Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis. Springer Vieweg, 2016.

Meisen, Philipp. Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis. Springer Vieweg, 2018.

Bogaerts, Kris, Arnošt Komárek, and Emmanuel Lesaffre. Survival Analysis with Interval-Censored Data. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315116945.

van den Hout, Ardo. Multi-State Survival Models for Interval-Censored Data. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/9781315374321.

The Statistical Analysis of Interval-censored Failure Time Data. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-37119-2.

Sun, Jianguo. The Statistical Analysis of Interval-censored Failure Time Data. Springer, 2010.