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Journal articles on the topic 'Normal models'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Martinec, Zedeněk, Karel Pěč, and M. Burša. "Normal density earth models." Studia Geophysica et Geodaetica 30, no. 2 (1986): 124–47. http://dx.doi.org/10.1007/bf01644373.

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2

Bernstein, Daniel Irving, and Seth Sullivant. "Normal Binary Hierarchical Models." Experimental Mathematics 26, no. 2 (2016): 153–64. http://dx.doi.org/10.1080/10586458.2016.1142911.

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3

Sullivant, Seth. "Normal binary graph models." Annals of the Institute of Statistical Mathematics 62, no. 4 (2010): 717–26. http://dx.doi.org/10.1007/s10463-010-0296-3.

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4

Arellano-Valle, R. B., H. Bolfarine, and V. H. Lachos. "Skew-normal Linear Mixed Models." Journal of Data Science 3, no. 4 (2021): 415–38. http://dx.doi.org/10.6339/jds.2005.03(4).238.

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5

Urbinati, Stefano. "Divisorial Models of Normal Varieties." Proceedings of the Edinburgh Mathematical Society 60, no. 4 (2017): 1053–64. http://dx.doi.org/10.1017/s0013091516000614.

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AbstractWe prove that the canonical ring of a canonical variety in the sense of de Fernex and Hacon is finitely generated. We prove that canonical varieties are Kawamata log terminal (klt) if and only if is finitely generated. We introduce a notion of nefness for non-ℚ-Gorenstein varieties and study some of its properties. We then focus on these properties for non-ℚ-Gorenstein toric varieties.

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6

Arellano-Valle, R. B., S. Ozan, H. Bolfarine, and V. H. Lachos. "Skew normal measurement error models." Journal of Multivariate Analysis 96, no. 2 (2005): 265–81. http://dx.doi.org/10.1016/j.jmva.2004.11.002.

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7

Krasiński, Andrzej. "Shear‐free normal cosmological models." Journal of Mathematical Physics 30, no. 2 (1989): 433–41. http://dx.doi.org/10.1063/1.528462.

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8

Kato, Kazuya, Chikara Nakayama, and Sampei Usui. "Néron models for admissible normal functions." Proceedings of the Japan Academy, Series A, Mathematical Sciences 90, no. 1 (2014): 6–10. http://dx.doi.org/10.3792/pjaa.90.6.

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9

Martínez-Flórez, Guillermo, Diego I. Gallardo, Osvaldo Venegas, Heleno Bolfarine, and Héctor W. Gómez. "Flexible Power-Normal Models with Applications." Mathematics 9, no. 24 (2021): 3183. http://dx.doi.org/10.3390/math9243183.

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The main object of this paper is to propose a new asymmetric model more flexible than the generalized Gaussian model. The probability density function of the new model can assume bimodal or unimodal shapes, and one of the parameters controls the skewness of the model. Three simulation studies are reported and two real data applications illustrate the flexibility of the model compared with traditional proposals in the literature.

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10

Miyaoka, Etsuo. "Estimation in Logistic Normal Linear Models." Japanese Journal of Biometrics 12 (1991): 99–104. http://dx.doi.org/10.5691/jjb.12.99.

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11

Everson, P. J., and C. N. Morris. "Inference for multivariate normal hierarchical models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62, no. 2 (2000): 399–412. http://dx.doi.org/10.1111/1467-9868.00239.

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12

CAPITANIO, A., A. AZZALINI, and E. STANGHELLINI. "Graphical models for skew-normal variates." Scandinavian Journal of Statistics 30, no. 1 (2003): 129–44. http://dx.doi.org/10.1111/1467-9469.00322.

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13

Basford, Kaye Enid. "Cluster analysis via normal mixture models." Bulletin of the Australian Mathematical Society 32, no. 3 (1985): 473–75. http://dx.doi.org/10.1017/s0004972700002586.

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14

Seo, Byungtae, and Daeyoung Kim. "Root selection in normal mixture models." Computational Statistics & Data Analysis 56, no. 8 (2012): 2454–70. http://dx.doi.org/10.1016/j.csda.2012.01.022.

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15

Bhargava, M., and M. Danard. "Normal mode initialization for simple models." Meteorology and Atmospheric Physics 60, no. 4 (1996): 225–36. http://dx.doi.org/10.1007/bf01042186.

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16

Basford, K. E., and G. J. McLachlan. "Likelihood Estimation with Normal Mixture Models." Applied Statistics 34, no. 3 (1985): 282. http://dx.doi.org/10.2307/2347474.

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17

Contreras Bravo, Leonardo Emiro, Jose Ignacio Rodriguez Molano, and Edwin Rivas Trujillo. "Exploring models of normal human locomotion." Contemporary Engineering Sciences 10, no. 30 (2017): 1457–71. http://dx.doi.org/10.12988/ces.2017.711176.

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18

Crowley, Evelyn M. "Product Partition Models for Normal Means." Journal of the American Statistical Association 92, no. 437 (1997): 192–98. http://dx.doi.org/10.1080/01621459.1997.10473616.

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19

Morris, Carl N., and Martin Lysy. "Shrinkage Estimation in Multilevel Normal Models." Statistical Science 27, no. 1 (2012): 115–34. http://dx.doi.org/10.1214/11-sts363.

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20

Toescu, Emil C. "Normal brain ageing: models and mechanisms." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1464 (2005): 2347–54. http://dx.doi.org/10.1098/rstb.2005.1771.

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Normal ageing is associated with a degree of decline in a number of cognitive functions. Apart from the issues raised by the current attempts to expand the lifespan, understanding the mechanisms and the detailed metabolic interactions involved in the process of normal neuronal ageing continues to be a challenge. One model, supported by a significant amount of experimental evidence, views the cellular ageing as a metabolic state characterized by an altered function of the metabolic triad: mitochondria–reactive oxygen species (ROS)–intracellular Ca 2+ . The perturbation in the relationship betwe

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21

González, Jorge, Francis Tuerlinckx, Paul De Boeck, and Ronald Cools. "Numerical integration in logistic-normal models." Computational Statistics & Data Analysis 51, no. 3 (2006): 1535–48. http://dx.doi.org/10.1016/j.csda.2006.05.003.

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22

Kahrari, F., C. S. Ferreira, and R. B. Arellano-Valle. "Skew-Normal-Cauchy Linear Mixed Models." Sankhya B 81, no. 2 (2018): 185–202. http://dx.doi.org/10.1007/s13571-018-0173-2.

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23

Hoffmann, K. H., Lishang Jiang, Wanghui Yu, and Ning Zhu. "Models of superconducting-normal-superconducting junctions." Mathematical Methods in the Applied Sciences 21, no. 1 (1998): 59–91. http://dx.doi.org/10.1002/(sici)1099-1476(19980110)21:1<59::aid-mma933>3.0.co;2-a.

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24

Terza, Joseph V. "Econometric models with normal polychotomous selectivity." Economics Letters 19, no. 2 (1985): 165–70. http://dx.doi.org/10.1016/0165-1765(85)90014-x.

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25

van der Gaag, L. C., and J. J. Ch Meyer. "Informational independence: Models and normal forms." International Journal of Intelligent Systems 13, no. 1 (1998): 83–109. http://dx.doi.org/10.1002/(sici)1098-111x(199801)13:1<83::aid-int7>3.0.co;2-t.

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26

Chikalo, Oleg, and Ilya Obukhov. "Models of Communication Channels Normal Behavior." Infocommunications and Radio Technologies 6, no. 1 (2023): 15–34. http://dx.doi.org/10.29039/2587-9936.2023.06.1.02.

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Models describing the normal operation of communication channels are considered. By using of such models is possible to identify abnormal behavior of telecommunications equipment and take the efforts that necessary to maintain its performance. To this goal “big data” generated by telemetry of telecommunications devices are analyzed.

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27

Cataño Velez, Elkin. "Robustez estadística." Lecturas de Economía, no. 24 (January 26, 2011): 85–99. http://dx.doi.org/10.17533/udea.le.n24a7769.

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• Resumen: El uso de modelos paramétricos estocásticos exactos tales como el normal, log-normal, exponencial, poisson, gama, etc. está hoy profundamente arraigado en la práctica estadística. La razón es que ellos permiten la representación aproximada de un conjunto de datos que puede ser fácilmente descrita e interpretada. Sin embargo, es bien conocido que el mundo real no se comporta tan bien como lo describen estos modelos. Recientemente surge una técnica estadística la cual emplea también los modelos paramétricos pero la inferencia es realizada para un entorno del modelo asumido. Es decir,

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28

Miao, Wang, Peng Ding, and Zhi Geng. "Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data." Journal of the American Statistical Association 111, no. 516 (2016): 1673–83. http://dx.doi.org/10.1080/01621459.2015.1105808.

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29

LONC, ZBIGNIEW, and MIROSŁAW TRUSZCZYŃSKI. "Computing minimal models, stable models and answer sets." Theory and Practice of Logic Programming 6, no. 4 (2006): 395–449. http://dx.doi.org/10.1017/s1471068405002607.

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We propose and study algorithms to compute minimal models, stable models and answer sets of $t$-CNF theories, and normal and disjunctive $t$-programs. We are especially interested in algorithms with non-trivial worst-case performance bounds. The bulk of the paper is concerned with the classes of 2- and 3-CNF theories, and normal and disjunctive 2- and 3-programs, for which we obtain significantly stronger results than those implied by our general considerations. We show that one can find all minimal models of 2-CNF theories and all answer sets of disjunctive 2-programs in time $O(m1\mbox{.}442

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30

Westermann, Gert, and Denis Mareschal. "Models of atypical development must also be models of normal development." Behavioral and Brain Sciences 25, no. 6 (2002): 771–72. http://dx.doi.org/10.1017/s0140525x02430130.

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Connectionist models aiming to reveal the mechanisms of atypical development must in their undamaged form constitute plausible models of normal development and follow a developmental trajectory that matches empirical data. Constructivist models that adapt their structure to the learning task satisfy this demand. They are therefore more informative in the study of atypical development than the static models employed by Thomas & Karmiloff-Smith (T&K-S).

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31

Hirsch, Katharina, Andreas Wienke, and Oliver Kuss. "Log-normal frailty models fitted as Poisson generalized linear mixed models." Computer Methods and Programs in Biomedicine 137 (December 2016): 167–75. http://dx.doi.org/10.1016/j.cmpb.2016.09.009.

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32

de Jesus, Vera, Sandra Saraiva Ferreira, and Joao Tiago Mexia. "Joint estimation for normal orthogonal mixed models." Discussiones Mathematicae Probability and Statistics 27, no. 1-2 (2007): 5. http://dx.doi.org/10.7151/dmps.1085.

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33

van der Kogel, A. "SP-0237 Necessity of normal tissue models." Radiotherapy and Oncology 161 (August 2021): S161. http://dx.doi.org/10.1016/s0167-8140(21)08531-5.

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34

Temperton, Clive. "Implicit Normal Mode Initialization for Spectral Models." Monthly Weather Review 117, no. 2 (1989): 436–51. http://dx.doi.org/10.1175/1520-0493(1989)117<0436:inmifs>2.0.co;2.

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35

Everson, Philip J. "Exact bayesian inference for normal hierarchical models." Journal of Statistical Computation and Simulation 68, no. 3 (2001): 223–41. http://dx.doi.org/10.1080/00949650108812068.

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36

Gómez, Héctor W., Hugo S. Salinas, and Heleno Bolfarine. "Generalized skew-normal models: properties and inference." Statistics 40, no. 6 (2006): 495–505. http://dx.doi.org/10.1080/02331880600723168.

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37

Majda, A. J., C. Franzke, and D. Crommelin. "Normal forms for reduced stochastic climate models." Proceedings of the National Academy of Sciences 106, no. 10 (2009): 3649–53. http://dx.doi.org/10.1073/pnas.0900173106.

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38

Betounes, David E. "Mean-curvature normal and dual-string models." Physical Review D 33, no. 12 (1986): 3634–39. http://dx.doi.org/10.1103/physrevd.33.3634.

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39

Patton, Thomas L. "Sandbox models of downward-steepening normal faults." AAPG Bulletin 89, no. 6 (2005): 781–97. http://dx.doi.org/10.1306/0105052001108.

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40

Pellonpää, Juha-Pekka, and Mikko Tukiainen. "Minimal normal measurement models of quantum instruments." Reports on Mathematical Physics 79, no. 3 (2017): 261–78. http://dx.doi.org/10.1016/s0034-4877(17)30040-x.

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41

Stark, Dudley. "OIL PRODUCTION MODELS WITH NORMAL RATE CURVES." Probability in the Engineering and Informational Sciences 25, no. 2 (2011): 205–17. http://dx.doi.org/10.1017/s0269964810000355.

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The normal curve has been used to fit the rate of both world and US oil production. In this article we give the first theoretical basis for these curve fittings. It is well known that oil field sizes can be modeled by independent samples from a lognormal distribution. We show that when field sizes are lognormally distributed, the starting time of the production of a field is approximately a linear function of the logarithm of its size, and production of a field occurs within a small enough time interval, then the resulting total rate of production is close to being a normal curve.

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42

Krishnan, Anita, Jodi I. Pike, Robert McCarter, et al. "Predictive Models for Normal Fetal Cardiac Structures." Journal of the American Society of Echocardiography 29, no. 12 (2016): 1197–206. http://dx.doi.org/10.1016/j.echo.2016.08.019.

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43

McLachlan, G. J., D. Peel, and W. J. Whiten. "Maximum likelihood clustering via normal mixture models." Signal Processing: Image Communication 8, no. 2 (1996): 105–11. http://dx.doi.org/10.1016/0923-5965(95)00039-9.

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44

Aghili Ashtiani, Arya, and Mohammad Bagher Menhaj. "Introducing $$g$$ g -normal fuzzy relational models." Soft Computing 19, no. 8 (2014): 2163–71. http://dx.doi.org/10.1007/s00500-014-1398-2.

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45

Gupta, Arjun K., and John T. Chen. "A class of multivariate skew-normal models." Annals of the Institute of Statistical Mathematics 56, no. 2 (2004): 305–15. http://dx.doi.org/10.1007/bf02530547.

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46

Došen, Kosta. "Models for stronger normal intuitionistic modal logics." Studia Logica 44, no. 1 (1985): 39–70. http://dx.doi.org/10.1007/bf00370809.

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47

Kelderman, H. "Measurement exchangeability and normal one-factor models." Biometrika 91, no. 3 (2004): 738–42. http://dx.doi.org/10.1093/biomet/91.3.738.

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48

Wang, H., and M. West. "Bayesian analysis of matrix normal graphical models." Biometrika 96, no. 4 (2009): 821–34. http://dx.doi.org/10.1093/biomet/asp049.

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49

Eslinger, Paul W., and Wayne A. Woodward. "Minimum hellinger distance estimation for normal models." Journal of Statistical Computation and Simulation 39, no. 1-2 (1991): 95–114. http://dx.doi.org/10.1080/00949659108811342.

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50

Fiedler, R. A. S., and A. W. Hood. "Numerical models of quiescent normal polarity prominences." Solar Physics 141, no. 1 (1992): 75–90. http://dx.doi.org/10.1007/bf00155905.

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