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Journal articles on the topic 'Mixed variables'

Author: Grafiati

Published: 1 June 2024

Last updated: 25 July 2025

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1

Daudin, J. J. "Selection of Variables in Mixed-Variable Discriminant Analysis." Biometrics 42, no. 3 (1986): 473. http://dx.doi.org/10.2307/2531198.

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2

ARAKAWA, Masao, Takaharu Shirai, Hitomi Kono, Hirotaka NAKAYAMA, and Hiroshi ISHIKAWA. "Approximate Optimization Using RBF : Mixed variable Optimization with Discrete Variables." Proceedings of Design & Systems Conference 2003.13 (2003): 108–11. http://dx.doi.org/10.1299/jsmedsd.2003.13.108.

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3

Vagabov, A. I., and A. H. Abud. "Variable separation method in solving multidimensional mixed problems with separable variables." Doklady Mathematics 89, no. 3 (2014): 263–66. http://dx.doi.org/10.1134/s1064562414030053.

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4

Hamid, Hashibah, Nor Idayu Mahat, and Safwati Ibrahim. "ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA." Journal of Information and Communication Technology 20, Number 3 (2021): 305–27. http://dx.doi.org/10.32890/jict2021.20.3.2.

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The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for ma

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5

Galicer, Daniel, Martín Mansilla, and Santiago Muro. "Mixed Bohr radius in several variables." Transactions of the American Mathematical Society 373, no. 2 (2019): 777–96. http://dx.doi.org/10.1090/tran/7870.

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6

Saracco, J., and M. Chavent. "Clustering of Variables for Mixed Data." EAS Publications Series 77 (2016): 121–69. http://dx.doi.org/10.1051/eas/1677007.

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7

Andrews, Bryan, Joseph Ramsey, and Gregory F. Cooper. "Scoring Bayesian networks of mixed variables." International Journal of Data Science and Analytics 6, no. 1 (2018): 3–18. http://dx.doi.org/10.1007/s41060-017-0085-7.

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8

Lijie, Cui, Lü Zhenzhou, and Li Guijie. "Reliability Analysis in Presence of Random Variables and Fuzzy Variables." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/365051.

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For mixed uncertainties of random variables and fuzzy variables in engineering, three indices, that is, interval reliability index, mean reliability index, and numerical reliability index, are proposed to measure safety of structure. Comparing to the reliability membership function for measuring the safety in case of mixed uncertainties, the proposed indices are more intuitive and easier to represent the safety degree of the engineering structure, and they are more suitable for the reliability design in the case of the mixed uncertainties. The differences and relations among three proposed ind

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9

Lee, Min Ho. "Mixed Jacobi-like forms of several variables." International Journal of Mathematics and Mathematical Sciences 2006 (2006): 1–14. http://dx.doi.org/10.1155/ijmms/2006/31542.

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We study mixed Jacobi-like forms of several variables associated to equivariant maps of the Poincaré upper half-plane in connection with usual Jacobi-like forms, Hilbert modular forms, and mixed automorphic forms. We also construct a lifting of a mixed automorphic form to such a mixed Jacobi-like form.

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10

Shim, Jooyong. "Kernel Poisson regression for mixed input variables." Journal of the Korean Data and Information Science Society 23, no. 6 (2012): 1231–39. http://dx.doi.org/10.7465/jkdi.2012.23.6.1231.

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11

NAKANISHI, Hiroko. "Discrimination on Mixed Binary and Continuous Variables." Japanese journal of applied statistics 22, no. 2 (1993): 51–65. http://dx.doi.org/10.5023/jappstat.22.51.

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12

Ngu, Penny, and Hashibah Hamid. "Systematic Literature Review of Mixed Variables Classification." International Journal of Data Science and Advanced Analytics 5, no. 5 (2022): 184–92. http://dx.doi.org/10.69511/ijdsaa.v5i5.177.

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Classification is one of the most popular approaches that had been used in a variety of fields. There are a lot of classification methods that are applicable to classify objects into their respective groups. Among the classification methods, the location model and smoothed location model received attention when the data consists of mixed continuous and categorical variables. In this paper, classification, location model and smoothed location model are screened from the Scopus and Science Direct websites for review purposes. A total of 70 articles were chosen through the systematic review proce

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13

McCane, Brendan, and Michael Albert. "Distance functions for categorical and mixed variables." Pattern Recognition Letters 29, no. 7 (2008): 986–93. http://dx.doi.org/10.1016/j.patrec.2008.01.021.

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14

Lee, Sanghak, Hyowon Kim, Jaehwan Kim, and Greg M. Allenby. "A choice model for mixed decision variables." Journal of Choice Modelling 28 (September 2018): 82–96. http://dx.doi.org/10.1016/j.jocm.2018.05.003.

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15

Yang, Miin-Shen, Pei-Yuan Hwang, and De-Hua Chen. "Fuzzy clustering algorithms for mixed feature variables." Fuzzy Sets and Systems 141, no. 2 (2004): 301–17. http://dx.doi.org/10.1016/s0165-0114(03)00072-1.

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16

Bairamov, Ismihan, and Safar Parsi. "Order statistics from mixed exchangeable random variables." Journal of Computational and Applied Mathematics 235, no. 16 (2011): 4629–38. http://dx.doi.org/10.1016/j.cam.2010.04.030.

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17

Adduri, Phani R., and Ravi C. Penmetsa. "System reliability analysis for mixed uncertain variables." Structural Safety 31, no. 5 (2009): 375–82. http://dx.doi.org/10.1016/j.strusafe.2009.02.001.

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18

Vichi, Maurizio, Donatella Vicari, and Henk A. L. Kiers. "Clustering and dimension reduction for mixed variables." Behaviormetrika 46, no. 2 (2019): 243–69. http://dx.doi.org/10.1007/s41237-018-0068-6.

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19

Krzanowski, W. J. "Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis." Computational Statistics & Data Analysis 19, no. 4 (1995): 419–31. http://dx.doi.org/10.1016/0167-9473(94)00011-7.

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20

Mahat, Nor Idayu, Wojtek Janusz Krzanowski, and Adolfo Hernandez. "Variable selection in discriminant analysis based on the location model for mixed variables." Advances in Data Analysis and Classification 1, no. 2 (2007): 105–22. http://dx.doi.org/10.1007/s11634-007-0009-9.

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21

Mbina Mbina, Alban, Guy Martial Nkiet, and Fulgence Eyi Obiang. "Variable selection in discriminant analysis for mixed continuous-binary variables and several groups." Advances in Data Analysis and Classification 13, no. 3 (2018): 773–95. http://dx.doi.org/10.1007/s11634-018-0343-0.

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22

Krusińska, Ewa. "New Procedure for Selection of Variables in Location Model for Mixed Variable Discrimination." Biometrical Journal 31, no. 5 (1989): 511–23. http://dx.doi.org/10.1002/bimj.4710310502.

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23

Jin, Shaobo, Fan Yang-Wallentin, and Kenneth A. Bollen. "A unified model-implied instrumental variable approach for structural equation modeling with mixed variables." Psychometrika 86, no. 2 (2021): 564–94. http://dx.doi.org/10.1007/s11336-021-09771-4.

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AbstractThe model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal . We develop a unified MIIV approach that applies to a mixture of binary, ordinal, censored, or continuous endogenous observed variables. We include estimates of factor loadings, regression coefficients, variances, and covariances along with their asymptotic standard erro

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24

Zeng, Xianyu, and Jianjun Wang. "Robust parameter design of latent variable Gaussian process model based on mixed input variables." IET Conference Proceedings 2024, no. 12 (2025): 1321–27. https://doi.org/10.1049/icp.2024.3626.

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25

Tulkin, Mamatov. "Mixed Fractional Integration In Mixed Weighted Generalized Hölder Spaces." International Journal of Case Studies 7, no. 6 (2018): 01–08. https://doi.org/10.5281/zenodo.3540325.

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26

Bishnoi, Sudha, and BK Hooda. "A survey of distance measures for mixed variables." International Journal of Chemical Studies 8, no. 4 (2020): 338–43. http://dx.doi.org/10.22271/chemi.2020.v8.i4f.10087.

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27

Rosenthal, H., and J. Binia. "On the epsilon entropy of mixed random variables." IEEE Transactions on Information Theory 34, no. 5 (1988): 1110–14. http://dx.doi.org/10.1109/18.21244.

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28

Cox, D. R., and Nanny Wermuth. "Likelihood Factorizations for Mixed Discrete and Continuous Variables." Scandinavian Journal of Statistics 26, no. 2 (1999): 209–20. http://dx.doi.org/10.1111/1467-9469.00145.

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29

Ichino, Manabu, and Jack Sklansky. "The relative neighborhood graph for mixed feature variables." Pattern Recognition 18, no. 2 (1985): 161–67. http://dx.doi.org/10.1016/0031-3203(85)90040-8.

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30

Dash, Sanjeeb, Santanu S. Dey, and Oktay Günlük. "On mixed-integer sets with two integer variables." Operations Research Letters 39, no. 5 (2011): 305–9. http://dx.doi.org/10.1016/j.orl.2011.06.010.

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31

Núñez, Marian, Angel Villarroya, and José María Oller. "Minimum Distance Probability Discriminant Analysis for Mixed Variables." Biometrics 59, no. 2 (2003): 248–53. http://dx.doi.org/10.1111/1541-0420.00031.

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32

Harring, Jeffrey R. "A Nonlinear Mixed Effects Model for Latent Variables." Journal of Educational and Behavioral Statistics 34, no. 3 (2009): 293–318. http://dx.doi.org/10.3102/1076998609332750.

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The nonlinear mixed effects model for continuous repeated measures data has become an increasingly popular and versatile tool for investigating nonlinear longitudinal change in observed variables. In practice, for each individual subject, multiple measurements are obtained on a single response variable over time or condition. This structure can be adapted to examine the change in latent variables rather than modeling change in manifest variables. This article considers a nonlinear mixed effects model for describing nonlinear change of a latent construct over time, where the latent construct of

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33

COX, D. R., and NANNY WERMUTH. "Response models for mixed binary and quantitative variables." Biometrika 79, no. 3 (1992): 441–61. http://dx.doi.org/10.1093/biomet/79.3.441.

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34

Elkhoraibi, Tarek, and Khalid M. Mosalam. "Towards error-free hybrid simulation using mixed variables." Earthquake Engineering & Structural Dynamics 36, no. 11 (2007): 1497–522. http://dx.doi.org/10.1002/eqe.691.

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35

Qu, Yunyun, and Jiwen Zeng. "Diophantine approximation with prime variables and mixed powers." Ramanujan Journal 52, no. 3 (2019): 625–39. http://dx.doi.org/10.1007/s11139-019-00167-8.

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36

Benders, J. F. "Partitioning procedures for solving mixed-variables programming problems." Computational Management Science 2, no. 1 (2005): 3–19. http://dx.doi.org/10.1007/s10287-004-0020-y.

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37

Li, Meng. "Diophantine Approximation with Prime Variables and Mixed Powers." Journal of Innovation and Development 10, no. 2 (2025): 63–70. https://doi.org/10.54097/dpg8r633.

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Let , , , , be non-zero real numbers, not all negative, and let be irrational and algebraic. Let be a well-spaced sequence and .We prove that for any given ,the number of satisfying and for which has no solution in primes , , , , does not exceed .

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38

Mamatov, Tulkin, Aloviddin Umarov, and Laylo Rustamova. "Mixed Fractional Differentiation Operators in Mixed Weighted Hölder Spaces." International Journal of Case Studies (ISSN Online 2305-509X) 08, no. 11 (2019): 113–18. https://doi.org/10.5281/zenodo.4841091.

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We study mixed fractional derivative of functions of two variables in weighted Hölder spaces of different orders in each variable. The obtained results extend the well-known theorem of Hardy-Littlewood for the one-dimensional fractional derivative to the case of mixed Hölderness

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39

Ortiz-Reyes, Alma Delia, Efraín Velasco-Bautista, Arian Correa-Díaz, and Gregorio Ángeles-Pérez. "Predicción de variables dasométricas mediante modelos lineales mixtos y datos de LiDAR aerotransportado." E-CUCBA 9, no. 17 (2021): 88–95. http://dx.doi.org/10.32870/ecucba.vi17.213.

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Adequate estimation of dasometric parameters such as basal area (AB), above-ground biomass (B), and timber volume (VOL) inmanaged forests is a primary requirement to quantify the role of forests in mitigation climate change mitigation. In this context,forest inventories represent the general technique to estimate dasometric parameters, however, they represent a greater consumptionof time and resources. Using data derived from remote sensors in the dasometric modeling offers huge possibilities as an auxiliarytool in forestry activities. The objective of this work was to obtain a statistical mod

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40

Shevchenko, Georgiy, and Lauri Viitasaari. "Adapted integral representations of random variables." International Journal of Modern Physics: Conference Series 36 (January 2015): 1560004. http://dx.doi.org/10.1142/s2010194515600046.

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We study integral representations of random variables with respect to general Hölder continuous processes and with respect to two particular cases; fractional Brownian motion and mixed fractional Brownian motion. We prove that an arbitrary random variable can be represented as an improper integral, and that the stochastic integral can have any distribution. If in addition the random variable is a final value of an adapted Hölder continuous process, then it can be represented as a proper integral. It is also shown that in the particular case of mixed fractional Brownian motion, any adapted rand

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41

Zhu, Chao Yan, Jing Yu Liu, Hong Yan Liu, and Xue Zhi Wang. "Complex Genetic Algorithm for Structure Shape Optimization Design of Mixed Discrete Variables." Applied Mechanics and Materials 166-169 (May 2012): 118–22. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.118.

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Discrete complex method is used in genetic algorithm(GA) and a mixed genetic algorithm called complex genetic algorithm(CGA) is formed. The complex method used here increases the quality of species groups, and improves the searching efficiency. The mixed genetic algorithm method is used in the shape optimization for mixed discrete variables. The integration and coding of the shape variables and the cross-section variables in genetic algorithm can not only solve the coupling problem of two kinds of variables, but also avoid the partial optimum solution resulting from the separation of the two k

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42

Hummel, Manuela, Dominic Edelmann, and Annette Kopp-Schneider. "Clustering of samples and variables with mixed-type data." PLOS ONE 12, no. 11 (2017): e0188274. http://dx.doi.org/10.1371/journal.pone.0188274.

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43

McCulloch, Charles. "Joint modelling of mixed outcome types using latent variables." Statistical Methods in Medical Research 17, no. 1 (2008): 53–73. http://dx.doi.org/10.1177/0962280207081240.

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After a brief review of the use of latent variables to accommodate the correlation among multiple outcomes of mixed types, through theoretical and numerical calculation, the consequences of such a construction are quantified. The effects of including latent variables on marginal inference in these models are contrasted with the situation for jointly normal outcomes. A simulation study illustrates the efficiency and reduction in bias gains possible in using joint models, and analysis of an example from the field of osteoarthritis illustrates potential practical differences.

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44

Zhou, Ling, Huazhen Lin, Xinyuan Song, and Yi Li. "Selection of Latent Variables for Multiple Mixed-outcome Models." Scandinavian Journal of Statistics 41, no. 4 (2014): 1064–82. http://dx.doi.org/10.1111/sjos.12084.

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45

Zarghami, Esmeail, and Hossein Bagheri. "Assessment of children's independent mobility variables by mixed method." Transportation Research Interdisciplinary Perspectives 8 (November 2020): 100239. http://dx.doi.org/10.1016/j.trip.2020.100239.

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46

Michalowicz, J. V., J. M. Nichols, F. Bucholtz, and C. C. Olson. "A general Isserlis theorem for mixed-Gaussian random variables." Statistics & Probability Letters 81, no. 8 (2011): 1233–40. http://dx.doi.org/10.1016/j.spl.2011.03.022.

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47

Beknazaryan, Aleksandr, Xin Dang, and Hailin Sang. "On mutual information estimation for mixed-pair random variables." Statistics & Probability Letters 148 (May 2019): 9–16. http://dx.doi.org/10.1016/j.spl.2018.12.011.

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48

Cui, Hengjian, Kai W. Ng, and Lixing Zhu. "Estimation in mixed effects model with errors in variables." Journal of Multivariate Analysis 91, no. 1 (2004): 53–73. http://dx.doi.org/10.1016/j.jmva.2004.04.014.

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49

Amir, H. M., and T. Hasegawa. "Shape optimization of skeleton structures using mixed-discrete variables." Structural Optimization 8, no. 2-3 (1994): 125–30. http://dx.doi.org/10.1007/bf01743308.

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50

Dube, Madhulika. "Mixed regression estimator under inclusion of some superfluous variables." Test 8, no. 2 (1999): 411–17. http://dx.doi.org/10.1007/bf02595878.

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