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Статті в журналах з теми "Mixed variables"

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Автор: Grafiati

Опубліковано: 1 червня 2024

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1

Daudin, J. J. "Selection of Variables in Mixed-Variable Discriminant Analysis." Biometrics 42, no. 3 (September 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 (May 2014): 263–66. http://dx.doi.org/10.1134/s1064562414030053.

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4

Galicer, Daniel, Martín Mansilla, and Santiago Muro. "Mixed Bohr radius in several variables." Transactions of the American Mathematical Society 373, no. 2 (November 5, 2019): 777–96. http://dx.doi.org/10.1090/tran/7870.

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5

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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6

Andrews, Bryan, Joseph Ramsey, and Gregory F. Cooper. "Scoring Bayesian networks of mixed variables." International Journal of Data Science and Analytics 6, no. 1 (January 11, 2018): 3–18. http://dx.doi.org/10.1007/s41060-017-0085-7.

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7

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 (June 11, 2021): 305–27. http://dx.doi.org/10.32890/jict2021.20.3.2.

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Анотація:

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 managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

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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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Анотація:

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 indices are investigated, and their applicability is compared. Furthermore, a technique based on the probability density function evolution method is employed to improve the computational efficiency of the proposed indices. At last, a numerical example and two engineering examples are illustrated to demonstrate the feasibility, reasonability, and efficiency of the computational technique of the proposed indices.

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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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Анотація:

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 (November 30, 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

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

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13

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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14

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

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15

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

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16

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

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17

Vichi, Maurizio, Donatella Vicari, and Henk A. L. Kiers. "Clustering and dimension reduction for mixed variables." Behaviormetrika 46, no. 2 (March 11, 2019): 243–69. http://dx.doi.org/10.1007/s41237-018-0068-6.

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18

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

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19

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 (July 17, 2007): 105–22. http://dx.doi.org/10.1007/s11634-007-0009-9.

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20

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 (September 21, 2018): 773–95. http://dx.doi.org/10.1007/s11634-018-0343-0.

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21

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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22

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 (June 2021): 564–94. http://dx.doi.org/10.1007/s11336-021-09771-4.

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Анотація:

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 errors. In addition, we create new goodness of fit tests of the model and overidentification tests of single equations. Our simulation study shows that the proposed MIIV approach is more robust to structural misspecifications than diagonally weighted least squares (DWLS) and that both the goodness of fit model tests and the overidentification equations tests can detect structural misspecifications. We also find that the bias in asymptotic standard errors for the MIIV estimators of factor loadings and regression coefficients are often lower than the DWLS ones, though the differences are small in large samples. Our analysis shows that scaling indicators with low reliability can adversely affect the MIIV estimators. Also, using a small subset of MIIVs reduces small sample bias of coefficient estimates, but can lower the power of overidentification tests of equations.

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23

Bishnoi, Sudha, and BK Hooda. "A survey of distance measures for mixed variables." International Journal of Chemical Studies 8, no. 4 (July 1, 2020): 338–43. http://dx.doi.org/10.22271/chemi.2020.v8.i4f.10087.

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24

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

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25

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

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26

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

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27

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

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28

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

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29

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

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Анотація:

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 interest is measured by multiple indicators gathered at each measurement occasion. To accomplish this, the nonlinear mixed effects model is modified to include a measurement model that explicitly expresses the relationship of the observed variables to the latent constructs. A method for marginal maximum likelihood estimation of this model is presented and discussed. An example using education data is provided to illustrate the utility of the model.

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30

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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31

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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32

Qu, Yunyun, and Jiwen Zeng. "Diophantine approximation with prime variables and mixed powers." Ramanujan Journal 52, no. 3 (August 20, 2019): 625–39. http://dx.doi.org/10.1007/s11139-019-00167-8.

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33

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

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34

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 (December 29, 2021): 88–95. http://dx.doi.org/10.32870/ecucba.vi17.213.

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Анотація:

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 model for each forest variable of interest: basal area,above-ground biomass and timber volume in a temperate forest under management in Zacualtipán, Hidalgo, Mexico, using linearmixed models and LiDAR (Light Detection And Ranging) data as predictor variables. For this, we consider that the cluster samplingunits have spatial correlation with respect to them distributed independently in the field. Metrics derived from LiDAR data wereused to fit the models. The metrics related to height and density of the vegetation presented the highest Pearson correlations (r = 0.52- 0.86) with the different dasometric variables and these were used as predictors in the adjusted models. The results indicated thatthe random effect of the cluster and the use of variance function significantly improved the heteroscedasticity, since the spatialcorrelation of the sites was included. This work showed the potential of using linear mixed models to take advantage of thedependency between sites in the same cluster and improve traditional estimates that do not model this hierarchical relationship.

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35

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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Анотація:

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 random variable can be represented as a proper integral.

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36

Hummel, Manuela, Dominic Edelmann, and Annette Kopp-Schneider. "Clustering of samples and variables with mixed-type data." PLOS ONE 12, no. 11 (November 28, 2017): e0188274. http://dx.doi.org/10.1371/journal.pone.0188274.

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37

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

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Анотація:

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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38

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 (April 3, 2014): 1064–82. http://dx.doi.org/10.1111/sjos.12084.

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39

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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40

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 (August 2011): 1233–40. http://dx.doi.org/10.1016/j.spl.2011.03.022.

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41

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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42

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

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43

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

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44

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

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45

Chen, Qiu-hua, Ping-shou Zhong, and Heng-jian Cui. "Empirical likelihood for mixed-effects error-in-variables model." Acta Mathematicae Applicatae Sinica, English Series 25, no. 4 (September 8, 2009): 561–78. http://dx.doi.org/10.1007/s10255-008-8805-3.

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46

Filomeno Coelho, Rajan. "Metamodels for mixed variables based on moving least squares." Optimization and Engineering 15, no. 2 (June 4, 2013): 311–29. http://dx.doi.org/10.1007/s11081-013-9216-8.

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47

Kuzovkov, E. G. "Graph model of an elastic body in mixed variables." Strength of Materials 18, no. 6 (June 1986): 807–13. http://dx.doi.org/10.1007/bf01523964.

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48

Sagratella, Simone. "Algorithms for generalized potential games with mixed-integer variables." Computational Optimization and Applications 68, no. 3 (July 18, 2017): 689–717. http://dx.doi.org/10.1007/s10589-017-9927-4.

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49

Fleury, Claude, and Vincent Braibant. "Structural optimization: A new dual method using mixed variables." International Journal for Numerical Methods in Engineering 23, no. 3 (March 1986): 409–28. http://dx.doi.org/10.1002/nme.1620230307.

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50

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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Анотація:

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 kinds of variables. The result of the exemplification indicates that the complex genetic algorithm for structure shape optimization design of mixed discrete variables is effective.

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