Relevant bibliographies by topics / Interval data

Academic literature on the topic 'Interval data'

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Published: 4 June 2021
Last updated: 31 May 2022

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Journal articles on the topic "Interval data"

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Santiago, Regivan, Flaulles Bergamaschi, Humberto Bustince, Graçaliz Dimuro, Tiago Asmus, and José Antonio Sanz. "On the Normalization of Interval Data." Mathematics 8, no. 11 (November 23, 2020): 2092. http://dx.doi.org/10.3390/math8112092.

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The impreciseness of numeric input data can be expressed by intervals. On the other hand, the normalization of numeric data is a usual process in many applications. How do we match the normalization with impreciseness on numeric data? A straightforward answer is that it is enough to apply a correct interval arithmetic, since the normalized exact value will be enclosed in the resulting “normalized” interval. This paper shows that this approach is not enough since the resulting “normalized” interval can be even wider than the input intervals. So, we propose a pair of axioms that must be satisfied by an interval arithmetic in order to be applied in the normalization of intervals. We show how some known interval arithmetics behave with respect to these axioms. The paper ends with a discussion about the current paradigm of interval computations.
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Izadikhah, Mohammad, Razieh Roostaee, and Ali Emrouznejad. "Fuzzy Data Envelopment Analysis with Ordinal and Interval Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 03 (May 27, 2021): 385–410. http://dx.doi.org/10.1142/s0218488521500173.

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In this paper, we reformulate the conventional DEA models as an imprecise DEA problem and propose a novel method for evaluating the DMUs when the inputs and outputs are fuzzy and/or ordinal or vary in intervals. For this purpose, we convert all data into interval data. In order to convert each fuzzy number into interval data, we use the nearest weighted interval approximation of fuzzy numbers by applying the weighting function, and we convert each ordinal data into interval one. In this manner, we could convert all data into interval data. The presented models determine the interval efficiencies for DMUs. To rank DMUs based on their associated interval efficiencies, we first apply the Ω-index that is developed for ranking of interval numbers. Then, by introducing an ideal DMU, we rank efficient DMUs to present a complete ranking. Finally, we use one example to illustrate the process and one real application in health care to show the usefulness of the proposed approach. For this evaluation, we consider interval, ordinal, and fuzzy data alongside the precise data to evaluate 38 hospitals selected by OIG. The results reveal the capabilities of the presented method to deal with the imprecise data.
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Meyer, Ronald M. "Ordinal Data Are Not Interval Data." Anesthesia & Analgesia 70, no. 5 (May 1990): 569???570. http://dx.doi.org/10.1213/00000539-199005000-00021.

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Shary, Sergey P. "Data fitting problem under interval uncertainty in data." Industrial laboratory. Diagnostics of materials 86, no. 1 (January 30, 2020): 62–74. http://dx.doi.org/10.26896/1028-6861-2020-86-1-62-74.

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We consider the data fitting problem under uncertainty, which is not described by probabilistic laws, but is limited in magnitude and has an interval character, i.e., is expressed by the intervals of possible data values. The most general case is considered when the intervals represent the measurement results both in independent (predictor) variables and in the dependent (criterial) variables. The concepts of weak and strong compatibility of data and parameters of functional dependence are introduced. It is shown that the resulting formulations of problems are reduced to the study and estimation of various solution sets for an interval system of equations constructed from the processed data. We discuss in detail the strong compatibility of the parameters and data, as more practical, more adequate to the reality and possessing better theoretical properties. The estimates of the function parameters, obtained in view of the strong compatibility, have a polynomial computational complexity, are robust, almost always have finite variability, and are also only partially affected by the so-called Demidenko paradox. We also propose a computational technology for solving the problem of constructing a linear functional dependence under interval data uncertainty and take into account the requirement of strong compatibility. It is based on the application of the so-called recognizing functional of the problem solution set — a special mapping, which recognizes, by the sign of the values, whether a point belongs to the solution set and simultaneously provides a quantitative measure of this membership. The properties of the recognizing functional are discussed. The maximum point of this functional is taken as an estimate of the parameters of the functional dependency under construction, which ensures the best compatibility between the parameters and data (or their least discrepancy). Accordingly, the practical implementation of this approach, named «maximum compatibility method», is reduced to the computation of the unconditional maximum of the recognizing functional — a concave non-smooth function. A specific example of solving the data fitting problem for a linear function from measurement data with interval uncertainty is presented.
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ISHIBUCHI, Hisao, Hideo TANAKA, and Kazunori NAGASAKA. "Interval Data Analysis by Revised Interval Regression Model." Transactions of the Society of Instrument and Control Engineers 25, no. 11 (1989): 1218–24. http://dx.doi.org/10.9746/sicetr1965.25.1218.

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Roy, Anuradha, and Daniel Klein. "Testing of mean interval for interval-valued data." Communications in Statistics - Theory and Methods 49, no. 20 (May 30, 2019): 5028–44. http://dx.doi.org/10.1080/03610926.2019.1612915.

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Alparslan Gök, S. Z., O. Palancı, and M. O. Olgun. "Cooperative interval games: Mountain situations with interval data." Journal of Computational and Applied Mathematics 259 (March 2014): 622–32. http://dx.doi.org/10.1016/j.cam.2013.01.021.

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Wang, Jie Fang, and Si Feng Liu. "Efficiency of DMUs with Interval Data under the Hypotheses of Weak Data Consistency." Advanced Materials Research 171-172 (December 2010): 86–89. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.86.

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The original data envelopment analysis (DEA) measures the efficiencies of decision making units（DMUs） with exact values of inputs and outputs. For interval data, some methods have been developed to calculate the interval efficiencies, however, the result of classic method always have high uncertainties. This paper proposes the hypothesis of weak data consistency in DEA. LP (linear programming) models to solve the upper and lower bounds of interval efficiencies are established. Lengths of efficiency intervals under the hypotheses are shorter the its limiting case is the result of DEA model under the hypotheses of data consistency
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Hron, Karel, Paula Brito, and Peter Filzmoser. "Exploratory data analysis for interval compositional data." Advances in Data Analysis and Classification 11, no. 2 (April 8, 2016): 223–41. http://dx.doi.org/10.1007/s11634-016-0245-y.

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D'Esposito, Maria R., Francesco Palumbo, and Giancarlo Ragozini. "Interval Archetypes: A New Tool for Interval Data Analysis." Statistical Analysis and Data Mining 5, no. 4 (March 22, 2012): 322–35. http://dx.doi.org/10.1002/sam.11140.

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Dissertations / Theses on the topic "Interval data"

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Oller, Piqué Ramon. "Survival analysis issues with interval-censored data." Doctoral thesis, Universitat Politècnica de Catalunya, 2006. http://hdl.handle.net/10803/6520.

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L'anàlisi de la supervivència s'utilitza en diversos àmbits per tal d'analitzar dades que mesuren el temps transcorregut entre dos successos. També s'anomena anàlisi de la història dels esdeveniments, anàlisi de temps de vida, anàlisi de fiabilitat o anàlisi del temps fins a l'esdeveniment. Una de les dificultats que té aquesta àrea de l'estadística és la presència de dades censurades. El temps de vida d'un individu és censurat quan només és possible mesurar-lo de manera parcial o inexacta. Hi ha diverses circumstàncies que donen lloc a diversos tipus de censura. La censura en un interval fa referència a una situació on el succés d'interès no es pot observar directament i només tenim coneixement que ha tingut lloc en un interval de temps aleatori. Aquest tipus de censura ha generat molta recerca en els darrers anys i usualment té lloc en estudis on els individus són inspeccionats o observats de manera intermitent. En aquesta situació només tenim coneixement que el temps de vida de l'individu es troba entre dos temps d'inspecció consecutius.

Aquesta tesi doctoral es divideix en dues parts que tracten dues qüestions importants que fan referència a dades amb censura en un interval. La primera part la formen els capítols 2 i 3 els quals tracten sobre condicions formals que asseguren que la versemblança simplificada pot ser utilitzada en l'estimació de la distribució del temps de vida. La segona part la formen els capítols 4 i 5 que es dediquen a l'estudi de procediments estadístics pel problema de k mostres. El treball que reproduïm conté diversos materials que ja s'han publicat o ja s'han presentat per ser considerats com objecte de publicació.

En el capítol 1 introduïm la notació bàsica que s'utilitza en la tesi doctoral. També fem una descripció de l'enfocament no paramètric en l'estimació de la funció de distribució del temps de vida. Peto (1973) i Turnbull (1976) van ser els primers autors que van proposar un mètode d'estimació basat en la versió simplificada de la funció de versemblança. Altres autors han estudiat la unicitat de la solució obtinguda en aquest mètode (Gentleman i Geyer, 1994) o han millorat el mètode amb noves propostes (Wellner i Zhan, 1997).

El capítol 2 reprodueix l'article d'Oller et al. (2004). Demostrem l'equivalència entre les diferents caracteritzacions de censura no informativa que podem trobar a la bibliografia i definim una condició de suma constant anàloga a l'obtinguda en el context de censura per la dreta. També demostrem que si la condició de no informació o la condició de suma constant són certes, la versemblança simplificada es pot utilitzar per obtenir l'estimador de màxima versemblança no paramètric (NPMLE) de la funció de distribució del temps de vida. Finalment, caracteritzem la propietat de suma constant d'acord amb diversos tipus de censura. En el capítol 3 estudiem quina relació té la propietat de suma constant en la identificació de la distribució del temps de vida. Demostrem que la distribució del temps de vida no és identificable fora de la classe dels models de suma constant. També demostrem que la probabilitat del temps de vida en cadascun dels intervals observables és identificable dins la classe dels models de suma constant. Tots aquests conceptes els
il·lustrem amb diversos exemples.

El capítol 4 s'ha publicat parcialment en l'article de revisió metodològica de Gómez et al. (2004). Proporciona una visió general d'aquelles tècniques que s'han aplicat en el problema no paramètric de comparació de dues o més mostres amb dades censurades en un interval. També hem desenvolupat algunes rutines amb S-Plus que implementen la versió permutacional del tests de Wilcoxon, Logrank i de la t de Student per a dades censurades en un interval (Fay and Shih, 1998). Aquesta part de la tesi doctoral es complementa en el capítol 5 amb diverses propostes d'extensió del test de Jonckeere. Amb l'objectiu de provar una tendència en el problema de k mostres, Abel (1986) va realitzar una de les poques generalitzacions del test de Jonckheere per a dades censurades en un interval. Nosaltres proposem altres generalitzacions d'acord amb els resultats presentats en el capítol 4. Utilitzem enfocaments permutacionals i de Monte Carlo. Proporcionem programes informàtics per a cada proposta i realitzem un estudi de simulació per tal de comparar la potència de cada proposta sota diferents models paramètrics i supòsits de tendència. Com a motivació de la metodologia, en els dos capítols s'analitza un conjunt de dades d'un estudi sobre els beneficis de la zidovudina en pacients en els primers estadis de la infecció del virus VIH (Volberding et al., 1995).

Finalment, el capítol 6 resumeix els resultats i destaca aquells aspectes que s'han de completar en el futur.
Survival analysis is used in various fields for analyzing data involving the duration between two events. It is also known as event history analysis, lifetime data analysis, reliability analysis or time to event analysis. One of the difficulties which arise in this area is the presence of censored data. The lifetime of an individual is censored when it cannot be exactly measured but partial information is available. Different circumstances can produce different types of censoring. Interval censoring refers to the situation when the event of interest cannot be directly observed and it is only known to have occurred during a random interval of time. This kind of censoring has produced a lot of work in the last years and typically occurs for individuals in a study being inspected or observed intermittently, so that an individual's lifetime is known only to lie between two successive observation times.

This PhD thesis is divided into two parts which handle two important issues of interval censored data. The first part is composed by Chapter 2 and Chapter 3 and it is about formal conditions which allow estimation of the lifetime distribution to be based on a well known simplified likelihood. The second part is composed by Chapter 4 and Chapter 5 and it is devoted to the study of test procedures for the k-sample problem. The present work reproduces several material which has already been published or has been already submitted.

In Chapter 1 we give the basic notation used in this PhD thesis. We also describe the nonparametric approach to estimate the distribution function of the lifetime variable. Peto (1973) and Turnbull (1976) were the first authors to propose an estimation method which is based on a simplified version of the likelihood function. Other authors have studied the uniqueness of the solution given by this method (Gentleman and Geyer, 1994) or have improved it with new proposals (Wellner and Zhan, 1997).

Chapter 2 reproduces the paper of Oller et al. (2004). We prove the equivalence between different characterizations of noninformative censoring appeared in the literature and we define an analogous constant-sum condition to the one derived in the context of right censoring. We prove as well that when the noninformative condition or the constant-sum condition holds, the simplified likelihood can be used to obtain the nonparametric maximum likelihood estimator (NPMLE) of the failure time distribution function. Finally, we characterize the constant-sum property according to different types of censoring. In Chapter 3 we study the relevance of the constant-sum property in the identifiability of the lifetime distribution. We show that the lifetime distribution is not identifiable outside the class of constant-sum models. We also show that the lifetime probabilities assigned to the observable intervals are identifiable inside the class of constant-sum models. We illustrate all these notions with several examples.

Chapter 4 has partially been published in the survey paper of Gómez et al. (2004). It gives a general view of those procedures which have been applied in the nonparametric problem of the comparison of two or more interval-censored samples. We also develop some S-Plus routines which implement the permutational version of the Wilcoxon test, the Logrank test and the t-test for interval censored data (Fay and Shih, 1998). This part of the PhD thesis is completed in Chapter 5 by different proposals of extension of the Jonckeere's test. In order to test for an increasing trend in the k-sample problem, Abel (1986) gives one of the few generalizations of the Jonckheree's test for interval-censored data. We also suggest different Jonckheere-type tests according to the tests presented in Chapter 4. We use permutational and Monte Carlo approaches. We give computer programs for each proposal and perform a simulation study in order compare the power of each proposal under different parametric assumptions and different alternatives. We motivate both chapters with the analysis of a set of data from a study of the benefits of zidovudine in patients in the early stages of the HIV infection (Volberding et al., 1995).

Finally, Chapter 6 summarizes results and address those aspects which remain to be completed.
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Long, Yongxian, and 龙泳先. "Semiparametric analysis of interval censored survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45541152.

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Gorelick, Jeremy Sun Jianguo. "Nonparametric analysis of interval-censored failure time data." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/7009.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. (Tony) Jianguo Sun. Includes bibliographical references.
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Zhang, Yue. "Bayesian Cox Models for Interval-Censored Survival Data." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479476510362603.

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Winarko, Edi, and edwin@ugm ac id. "The Discovery and Retrieval of Temporal Rules in Interval Sequence Data." Flinders University. Informatics and Engineering, 2007. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20080107.164033.

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Data mining is increasingly becoming important tool in extracting interesting knowledge from large databases. Many industries are now using data mining tools for analysing their large collections of databases and making business decisions. Many data mining problems involve temporal aspects, with examples ranging from engineering to scientific research, finance and medicine. Temporal data mining is an extension of data mining which deals with temporal data. Mining temporal data poses more challenges than mining static data. While the analysis of static data sets often comes down to the question of data items, with temporal data there are many additional possible relations. One of the tasks in temporal data mining is the pattern discovery task, whose objective is to discover time-dependent correlations, patterns or rules between events in large volumes of data. To date, most temporal pattern discovery research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. This thesis addresses several issues related to the pattern discovery from interval sequence data. Despite its importance, this area of research has received relatively little attention and there are still many issues that need to be addressed. Three main issues that this thesis considers include the definition of what constitutes an interesting pattern in interval sequence data, the efficient mining for patterns in the data, and the identification of interesting patterns from a large number of discovered patterns. In order to deal with these issues, this thesis formulates the problem of discovering rules, which we term richer temporal association rules, from interval sequence databases. Furthermore, this thesis develops an efficient algorithm, ARMADA, for discovering richer temporal association rules. The algorithm does not require candidate generation. It utilizes a simple index, and only requires at most two database scans. In this thesis, a retrieval system is proposed to facilitate the selection of interesting rules from a set of discovered richer temporal association rules. To this end, a high-level query language specification, TAR-QL, is proposed to specify the criteria of the rules to be retrieved from the rule sets. Three low-level methods are developed to evaluate queries involving rule format conditions. In order to improve the performance of the methods, signature file based indexes are proposed. In addition, this thesis proposes the discovery of inter-transaction relative temporal association rules from event sequence databases.
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Shuma, Mercy Violet 1957. "Design of a microcomputer "time interval board" for time interval statistical analysis of nuclear systems." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276685.

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A microcomputer based hardware, the Time Interval Board, was designed and the software interface control program was developed. The board measures time intervals between consecutive pulses from a discriminator output. The data is stored in on-board 16K x 16 memory. The microcomputer empties and processes the data when the on-board memory is filled. Data collection continues until the preset collection period is finished or a forced end is initiated. During this period, control is passed between the hardware and the microcomputer via the interface circuit. The designed hardware is IBM PC compatible.
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Wang, Lianming. "Statistical analysis of multivariate interval-censored failure time data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4375.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.
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Lim, Hee-Jeong. "Statistical analysis of interval-censored and truncated survival data /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025635.

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Zhao, Qiang. "Nonparametric treatment comparisons for interval-censored failure time data /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144474.

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Chen, Man-Hua. "Statistical analysis of multivariate interval-censored failure time data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4776.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 6, 2009) Includes bibliographical references.
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Books on the topic "Interval data"

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

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

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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.

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

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

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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.

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

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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.

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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.

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

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Book chapters on the topic "Interval data"

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Koncilia, Christian, Tadeusz Morzy, Robert Wrembel, and Johann Eder. "Interval OLAP: Analyzing Interval Data." In Data Warehousing and Knowledge Discovery, 233–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10160-6_21.

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Kent, Raymond. "Summarising Interval Variables." In Data Construction and Data Analysis for Survey Research, 116–37. London: Macmillan Education UK, 2001. http://dx.doi.org/10.1007/978-1-137-08944-1_7.

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Demortier, Luc. "Interval Estimation." In Data Analysis in High Energy Physics, 107–51. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527653416.ch4.

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Allahviranloo, Tofigh, Witold Pedrycz, and Armin Esfandiari. "Interval Interpolation." In Advances in Numerical Analysis Emphasizing Interval Data, 131–45. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003218173-5.

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Meisen, Philipp. "Time Interval Data Analysis." In Analyzing Time Interval Data, 7–44. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15728-9_2.

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Collett, D. "Interval-censored survival data." In Modelling Survival Data in Medical Research, 237–51. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3115-3_8.

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Berry, Kenneth J., Paul W. Mielke, and Janis E. Johnston. "Randomized Designs: Interval Data." In Permutation Statistical Methods, 57–113. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28770-6_3.

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Meisen, Philipp, Diane Keng, Tobias Meisen, Marco Recchioni, and Sabina Jeschke. "Querying Time Interval Data." In Enterprise Information Systems, 45–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-29133-8_3.

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Meisen, Philipp. "Introduction and Motivation." In Analyzing Time Interval Data, 1–5. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15728-9_1.

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Meisen, Philipp. "State of the Art." In Analyzing Time Interval Data, 45–71. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15728-9_3.

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Conference papers on the topic "Interval data"

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Chuang, Chen-Chia, Chin-Wen Li, Chih-Ching Hsiao, Shun-Feng Su, and Jin-Tsong Jeng. "Robust interval support vector interval regression networks for interval-valued data with outliers." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044510.

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Moerchen, Fabian, and Dmitriy Fradkin. "Robust mining of time intervals with semi-interval partial order patterns." In Proceedings of the 2010 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611972801.28.

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Peng, Wei, and Tao Li. "Interval Data Clustering with Applications." In 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06). IEEE, 2006. http://dx.doi.org/10.1109/ictai.2006.71.

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Billard, Lynne. "Some analyses of interval data." In 2008 30th International Conference on Information Technology Interfaces (ITI). IEEE, 2008. http://dx.doi.org/10.1109/iti.2008.4588377.

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Keel, L. H., and S. P. Bhattacharyya. "Data based interval controller design." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434467.

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Guo, Junpeng, Wenhua Li, and Sue Cheng. "Normalization of interval symbolic data." In EM). IEEE, 2009. http://dx.doi.org/10.1109/icieem.2009.5344314.

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Benavoli, A. "Interval dominance based data association." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711910.

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Li, Shuxin, Robert Lee, and Sheau-Dong Lang. "Detecting outliers in interval data." In the 44th annual southeast regional conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1185448.1185514.

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Miller, R. J., and Y. Yang. "Association rules over interval data." In the 1997 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/253260.253361.

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Boukhris, A. "Data validation using interval algebra." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980263.

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Reports on the topic "Interval data"

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C.R. Wilson and T.A. Grant. Data Qulaification Report Flowong Interval Data for Use On the Yucca Mountain Project. Office of Scientific and Technical Information (OSTI), August 2000. http://dx.doi.org/10.2172/893391.

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Kreinovich, Vladik, William Louis Oberkampf, Lev Ginzburg, Scott Ferson, and Janos Hajagos. Experimental uncertainty estimation and statistics for data having interval uncertainty. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/910198.

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Taasevigen, Danny J., Srinivas Katipamula, and William Koran. Interval Data Analysis with the Energy Charting and Metrics Tool (ECAM). Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1028580.

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Wong, George. Cox Model for Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada396730.

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Wong, George Y. Cox Model for Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada408771.

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Wong, George Y. Cox Model for Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada428542.

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Wong, George Y. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418647.

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Wong, George Y. Statistical Analysis of Multivariate Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada409921.

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Wong, George Y. Cox Regression Model for Interval-Censored Data in Breast Cancer Follow-up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada419260.

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Wong, George. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390768.

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