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Journal articles on the topic 'Interval data'

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Published: 4 June 2021

Last updated: 31 May 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Feng, Jianglin, Aakrosh Ratan, and Nathan C. Sheffield. "Augmented Interval List: a novel data structure for efficient genomic interval search." Bioinformatics 35, no. 23 (May 31, 2019): 4907–11. http://dx.doi.org/10.1093/bioinformatics/btz407.

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Abstract Motivation Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary. Results We present a new data structure, the Augmented Interval List (AIList), to enumerate intersections between a query interval q and an interval set R. An AIList is constructed by first sorting R as a list by the interval start coordinate, then decomposing it into a few approximately flattened components (sublists), and then augmenting each sublist with the running maximum interval end. The query time for AIList is O(log2N+n+m), where n is the number of overlaps between R and q, N is the number of intervals in the set R and m is the average number of extra comparisons required to find the n overlaps. Tested on real genomic interval datasets, AIList code runs 5–18 times faster than standard high-performance code based on augmented interval-trees, nested containment lists or R-trees (BEDTools). For large datasets, the memory-usage for AIList is 4–60% of other methods. The AIList data structure, therefore, provides a significantly improved fundamental operation for highly scalable genomic data analysis. Availability and implementation An implementation of the AIList data structure with both construction and search algorithms is available at http://ailist.databio.org. Supplementary information Supplementary data are available at Bioinformatics online.

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12

Entani, Tomoe. "Interval Data Envelopment Analysis for Inter-Group Data Usage." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (January 20, 2020): 113–22. http://dx.doi.org/10.20965/jaciii.2020.p0113.

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Organizations are interested in exploiting the data from the other organizations for better analyses. Therefore, the data related policies of organizations should be sensitive to the data privacy issue, which has been widely discussed recently. The present study is focused on inter-group data usage for a relative evaluation. This research is based on the data envelopment analysis (DEA), which is used to measure the efficiency of a decision making unit (DMU) relatively employed within a group. In DEA, establishing an efficient frontier consisting of efficient DMUs is essential. We can obtain the efficiency values of a DMU by projecting it to the efficient frontier, and including in the efficiency interval via the interval DEA. When the original data of multiple groups are not open to each other, the alternative is to exchange the information corresponding to the efficient frontiers to estimate the efficiency intervals of a DMU in such a manner that the alternative is in the other groups. Therefore, in this paper, we propose a method to replace the efficient frontier with a weight vector set, from which it is not possible to reconstruct the original data. Considering the weight vector sets of multiple groups, a DMU has three types of efficiency intervals: in its own group, in each of the other groups, and in the integrated group. They provide rich insights on the DMU from a broad perspective, and this encourages inter-group data usage. In this process, we focus on two types of information reduction: one is from the efficient frontier to the weight vector set, and the other is from a union of the groups to the integrated group.

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13

Zaman, Kais, and Saraf Anika Kritee. "An Optimization-Based Approach to Calculate Confidence Interval on Mean Value with Interval Data." Journal of Optimization 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/768932.

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In this paper, we propose a methodology for construction of confidence interval on mean values with interval data for input variable in uncertainty analysis and design optimization problems. The construction of confidence interval with interval data is known as a combinatorial optimization problem. Finding confidence bounds on the mean with interval data has been generally considered an NP hard problem, because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimization to find the confidence interval on mean values with interval data. With numerical experimentation, we show that the proposed confidence bound algorithms are scalable in polynomial time with respect to increasing number of intervals. Several sets of interval data with different numbers of intervals and type of overlap are presented to demonstrate the proposed methods. As against the current practice for the design optimization with interval data that typically implements the constraints on interval variables through the computation of bounds on mean values from the sampled data, the proposed approach of construction of confidence interval enables more complete implementation of design optimization under interval uncertainty.

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14

Brito, Paula, A. Pedro Duarte Silva, and José G. Dias. "Probabilistic clustering of interval data." Intelligent Data Analysis 19, no. 2 (April 14, 2015): 293–313. http://dx.doi.org/10.3233/ida-150718.

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15

Billard, Lynne. "Some Analyses of Interval Data." Journal of Computing and Information Technology 16, no. 4 (2008): 225. http://dx.doi.org/10.2498/cit.1001390.

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16

Shao, Wei, Flora D. Salim, Andy Song, and Athman Bouguettaya. "Clustering Big Spatiotemporal-Interval Data." IEEE Transactions on Big Data 2, no. 3 (September 1, 2016): 190–203. http://dx.doi.org/10.1109/tbdata.2016.2599923.

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17

Beckman, Steve, W. James Smith, and Buhong Zheng. "Measuring inequality with interval data." Mathematical Social Sciences 58, no. 1 (July 2009): 25–34. http://dx.doi.org/10.1016/j.mathsocsci.2009.01.001.

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18

Lindsey, Jane C., and Louise M. Ryan. "Methods for interval-censored data." Statistics in Medicine 17, no. 2 (January 30, 1998): 219–38. http://dx.doi.org/10.1002/(sici)1097-0258(19980130)17:2<219::aid-sim735>3.0.co;2-o.

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19

Miller, R. J., and Y. Yang. "Association rules over interval data." ACM SIGMOD Record 26, no. 2 (June 1997): 452–61. http://dx.doi.org/10.1145/253262.253361.

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20

RABINOWITZ, DANIEL, ANASTASIOS TSIATIS, and JORGE ARAGON. "Regression with interval-censored data." Biometrika 82, no. 3 (1995): 501–13. http://dx.doi.org/10.1093/biomet/82.3.501.

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21

Lorentzos, N. A., and Y. G. Mitsopoulos. "SQL extension for interval data." IEEE Transactions on Knowledge and Data Engineering 9, no. 3 (1997): 480–99. http://dx.doi.org/10.1109/69.599935.

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22

Kumkov, Sergey I., Vyacheslav S. Nikitin, Tatyana N. Ostanina, and Valentin M. Rudoy. "Interval processing of electrochemical data." Journal of Computational and Applied Mathematics 380 (December 2020): 112961. http://dx.doi.org/10.1016/j.cam.2020.112961.

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23

Liu, Shiang-Tai, and Chiang Kao. "Matrix games with interval data." Computers & Industrial Engineering 56, no. 4 (May 2009): 1697–700. http://dx.doi.org/10.1016/j.cie.2008.06.002.

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24

Duarte Silva, A. Pedro, Peter Filzmoser, and Paula Brito. "Outlier detection in interval data." Advances in Data Analysis and Classification 12, no. 3 (December 15, 2017): 785–822. http://dx.doi.org/10.1007/s11634-017-0305-y.

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25

Magnani, Matteo, and Danilo Montesi. "Management of interval probabilistic data." Acta Informatica 45, no. 2 (November 21, 2007): 93–130. http://dx.doi.org/10.1007/s00236-007-0065-9.

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26

Jahanshahloo, G. R., F. Hosseinzadeh Lotfi, and S. Sohraiee. "Egoist’s dilemma with interval data." Applied Mathematics and Computation 183, no. 1 (December 2006): 94–105. http://dx.doi.org/10.1016/j.amc.2006.05.058.

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27

Burke, John M., Charles P. Solomon, and Charles B. Seelig. "Ordinal and interval data analysis." Journal of General Internal Medicine 7, no. 5 (September 1992): 567. http://dx.doi.org/10.1007/bf02599468.

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28

Beresteanu, Arie, and Yuya Sasaki. "Quantile regression with interval data." Econometric Reviews 40, no. 6 (July 3, 2021): 562–83. http://dx.doi.org/10.1080/07474938.2021.1889201.

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29

Izadikhah, Mohammad. "Context-Dependent Data Envelopment Analysis with Interval Data." American Journal of Computational Mathematics 01, no. 04 (2011): 256–63. http://dx.doi.org/10.4236/ajcm.2011.14031.

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30

Lotfi, F. Hosseinzadeh, G. R. Jahanshahloo, M. Izadikhah, and M. Esmaeili. "Ranking DMUs with interval data using interval super efficiency index." International Mathematical Forum 2 (2007): 413–20. http://dx.doi.org/10.12988/imf.2007.07037.

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31

Remesan, Renji, Azadeh Ahmadi, Muhammad Ali Shamim, and Dawei Han. "Effect of data time interval on real-time flood forecasting." Journal of Hydroinformatics 12, no. 4 (February 2, 2010): 396–407. http://dx.doi.org/10.2166/hydro.2010.063.

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Rainfall–runoff is a complicated nonlinear process and many data mining tools have demonstrated their powerful potential in its modelling but still there are many unsolved problems. This paper addresses a mostly ignored area in hydrological modelling: data time interval for models. Modern data collection and telecommunication technologies can provide us with very high resolution data with extremely fine sampling intervals. We hypothesise that both too large and too small time intervals would be detrimental to a model's performance, which has been illustrated in the case study. It has been found that there is an optimal time interval which is different from the original data time interval (i.e. the measurement time interval). It has been found that the data time interval does have a major impact on the model's performance, which is more prominent for longer lead times than for shorter ones. This is highly relevant to flood forecasting since a flood modeller usually tries to stretch his/her model's lead time as far as possible. If the selection of data time interval is not considered, the model developed will not be performing at its full potential. The application of the Gamma Test and Information Entropy introduced in this paper may help the readers to speed up their data input selection process.

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32

Jin, Yan, and Jiang Hong Ma. "An Interval Slope Approach to Fuzzy C-Means Clustering Algorithm for Interval Valued Data." Advanced Materials Research 989-994 (July 2014): 1641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1641.

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Interval data is a range of continuous values which can describe the uncertainty. The traditional clustering methods ignore the structure information of intervals. And some modified ones have been developed. We have already used Taylor technique to perform well in the fuzzy c-means clustering algorithm. In this paper, we propose a new way based on the mixed interval slopes technique and interval computing. Experimental results also show the effectiveness of our approach.

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33

Park, Sang Woo, David Champredon, and Jonathan Dushoff. "Inferring generation-interval distributions from contact-tracing data." Journal of The Royal Society Interface 17, no. 167 (June 2020): 20190719. http://dx.doi.org/10.1098/rsif.2019.0719.

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Generation intervals, defined as the time between when an individual is infected and when that individual infects another person, link two key quantities that describe an epidemic: the initial reproductive number, R initial , and the initial rate of exponential growth, r . Generation intervals can be measured through contact tracing by identifying who infected whom. We study how realized intervals differ from ‘intrinsic’ intervals that describe individual-level infectiousness and identify both spatial and temporal effects, including truncating (due to observation time), and the effects of susceptible depletion at various spatial scales. Early in an epidemic, we expect the variation in the realized generation intervals to be mainly driven by truncation and by the population structure near the source of disease spread; we predict that correcting realized intervals for the effect of temporal truncation but not for spatial effects will provide the initial forward generation-interval distribution, which is spatially informed and correctly links r and R initial . We develop and test statistical methods for temporal corrections of generation intervals, and confirm our prediction using individual-based simulations on an empirical network.

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34

Bohn, Mary Kathryn, and Khosrow Adeli. "Application of the TML method to big data analytics and reference interval harmonization." Journal of Laboratory Medicine 45, no. 2 (January 22, 2021): 79–85. http://dx.doi.org/10.1515/labmed-2020-0133.

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Abstract Significant variation in reported reference intervals across healthcare centers and networks for many well-standardized laboratory tests continues to exist, negatively impacting patient outcomes by increasing the risk of inappropriate and inconsistent test result interpretation. Reference interval harmonization has been limited by challenges associated with direct reference interval establishment as well as hesitancies to apply currently available indirect methodologies. The Truncated Maximum Likelihood (TML) method for indirect reference interval establishment developed by the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) presents unique clinical and statistical advantages compared to traditional indirect methods (Hoffmann and Bhattacharya), increasing the feasibility of developing indirect reference intervals that are comparable to those determined using a direct a priori approach based on healthy reference populations. Here, we review the application of indirect methods, particularly the TML method, to reference interval harmonization and discuss their associated advantages and disadvantages. We also describe the CSCC Reference Interval Harmonization Working Group’s experience with the application of the TML method in harmonization of adult reference intervals in Canada.

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35

Halme, Merja, Tarja Joro, and Matti Koivu. "Dealing with interval scale data in data envelopment analysis." European Journal of Operational Research 137, no. 1 (February 2002): 22–27. http://dx.doi.org/10.1016/s0377-2217(01)00090-x.

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36

Shary, S. P. "STRONG COMPATABILITY IN DATA FITTING PROBLEMS WITH INTERVAL DATA." Bulletin of the South Ural State University series "Mathematics. Mechanics. Physics" 9, no. 1 (2017): 39–48. http://dx.doi.org/10.14529/mmph170105.

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37

Harwell, Michael R., and Guido G. Gatti. "Rescaling Ordinal Data to Interval Data in Educational Research." Review of Educational Research 71, no. 1 (March 2001): 105–31. http://dx.doi.org/10.3102/00346543071001105.

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38

Nasution, N. H., S. Efendi, and Tulus. "Sensitivity Analysis in Data Envelopment Analysis for Interval Data." Journal of Physics: Conference Series 1255 (August 2019): 012084. http://dx.doi.org/10.1088/1742-6596/1255/1/012084.

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39

Inuiguchi, Masahiro, and Fumiki Mizoshita. "Qualitative and quantitative data envelopment analysis with interval data." Annals of Operations Research 195, no. 1 (October 4, 2011): 189–220. http://dx.doi.org/10.1007/s10479-011-0988-y.

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40

Blanco-Fernández, Angela, and Peter Winker. "Data generation processes and statistical management of interval data." AStA Advances in Statistical Analysis 100, no. 4 (June 30, 2016): 475–94. http://dx.doi.org/10.1007/s10182-016-0274-z.

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41

Kooperberg, Charles, and Douglas B. Clarkson. "Hazard Regression with Interval-Censored Data." Biometrics 53, no. 4 (December 1997): 1485. http://dx.doi.org/10.2307/2533514.

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42

Billard, L., and J. Le-Rademacher. "Principal component analysis for interval data." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 6 (September 18, 2012): 535–40. http://dx.doi.org/10.1002/wics.1231.

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43

J., Girish Raguvir, Manas Jyoti Kashyop, and N. S. Narayanaswamy. "Dynamic data structures for interval coloring." Theoretical Computer Science 838 (October 2020): 126–42. http://dx.doi.org/10.1016/j.tcs.2020.06.024.

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44

Hendrickx, Wouter, Dirk Deschrijver, Luc Knockaert, and Tom Dhaene. "Magnitude Vector Fitting to interval data." Mathematics and Computers in Simulation 80, no. 3 (November 2009): 572–80. http://dx.doi.org/10.1016/j.matcom.2009.09.009.

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45

González-Rivera, Gloria, and Wei Lin. "Constrained Regression for Interval-Valued Data." Journal of Business & Economic Statistics 31, no. 4 (October 2013): 473–90. http://dx.doi.org/10.1080/07350015.2013.818004.

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46

HELWICK, CAROLINE. "Data Murky on Interval Between Sigmoidoscopies." Internal Medicine News 44, no. 11 (June 2011): 38. http://dx.doi.org/10.1016/s1097-8690(11)70565-0.

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47

Sato-Ilic, Mika. "Symbolic Clustering with Interval-Valued Data." Procedia Computer Science 6 (2011): 358–63. http://dx.doi.org/10.1016/j.procs.2011.08.066.

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48

Pini, A., and S. Vantini. "Interval-wise testing for functional data." Journal of Nonparametric Statistics 29, no. 2 (March 27, 2017): 407–24. http://dx.doi.org/10.1080/10485252.2017.1306627.

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49

Muravyov, Sergey V., Liudmila I. Khudonogova, and Ekaterina Y. Emelyanova. "Interval data fusion with preference aggregation." Measurement 116 (February 2018): 621–30. http://dx.doi.org/10.1016/j.measurement.2017.08.045.

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50

Quan, Hui, and Kai F. Yu. "Sequential testing with interval censored data∗." Journal of Statistical Computation and Simulation 51, no. 2-4 (February 1995): 239–47. http://dx.doi.org/10.1080/00949659508811635.

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