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Articoli di riviste sul tema "Finite mixtures"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 1 febbraio 2022

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1

Villani, Mattias, Robert Kohn, and David J. Nott. "Generalized smooth finite mixtures." Journal of Econometrics 171, no. 2 (2012): 121–33. http://dx.doi.org/10.1016/j.jeconom.2012.06.012.

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2

Tamandi, Mostafa, and Ahad Jamalizadeh. "Finite mixture modeling using shape mixtures of the skew scale mixtures of normal distributions." Communications in Statistics - Simulation and Computation 49, no. 12 (2019): 3345–66. http://dx.doi.org/10.1080/03610918.2018.1547397.

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3

Wood, G. R. "Binomial Mixtures and Finite Exchangeability." Annals of Probability 20, no. 3 (1992): 1167–73. http://dx.doi.org/10.1214/aop/1176989684.

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4

Shamilov, Aladdin, and Senay Asma. "Finite mixtures of MaxEnt distributions." Applied Mathematics and Computation 206, no. 2 (2008): 530–37. http://dx.doi.org/10.1016/j.amc.2008.05.058.

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5

Orellana, Rafael, Rodrigo Carvajal, and Juan C. Agüero. "Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures." IFAC-PapersOnLine 51, no. 15 (2018): 706–11. http://dx.doi.org/10.1016/j.ifacol.2018.09.200.

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6

Karki, Pravat, Yong-Rak Kim, and Dallas N. Little. "Dynamic Modulus Prediction of Asphalt Concrete Mixtures through Computational Micromechanics." Transportation Research Record: Journal of the Transportation Research Board 2507, no. 1 (2015): 1–9. http://dx.doi.org/10.3141/2507-01.

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This paper presents a computational micromechanics modeling approach to predict the dynamic modulus of asphalt concrete mixtures. The modeling uses a finite element method combined with the micromechanical representative volume element (RVE) of mixtures and laboratory tests that characterize the properties of individual mixture constituents. The model treats asphalt concrete mixtures as heterogeneous with two primary phases: a linear viscoelastic fine aggregate matrix (FAM) phase and a linear elastic aggregate phase. The mechanical properties of each phase were experimentally obtained by condu

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7

Tzougas, George, Spyridon Vrontos, and Nicholas Frangos. "OPTIMAL BONUS-MALUS SYSTEMS USING FINITE MIXTURE MODELS." ASTIN Bulletin 44, no. 2 (2014): 417–44. http://dx.doi.org/10.1017/asb.2013.31.

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AbstractThis paper presents the design of optimal Bonus-Malus Systems using finite mixture models, extending the work of Lemaire (1995; Lemaire, J. (1995) Bonus-Malus Systems in Automobile Insurance. Norwell, MA: Kluwer) and Frangos and Vrontos (2001; Frangos, N. and Vrontos, S. (2001) Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. ASTIN Bulletin, 31(1), 1–22). Specifically, for the frequency component we employ finite Poisson, Delaporte and Negative Binomial mixtures, while for the severity component we employ fi

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8

Seshadri, V. "Finite mixtures of natural exponential families." Canadian Journal of Statistics 19, no. 4 (1991): 437–45. http://dx.doi.org/10.2307/3315433.

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9

Karlis, Dimitris, and Evdokia Xekalaki. "Robust inference for finite poisson mixtures." Journal of Statistical Planning and Inference 93, no. 1-2 (2001): 93–115. http://dx.doi.org/10.1016/s0378-3758(00)00207-x.

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10

Gassiat, Elisabeth, and Ramon van Handel. "The local geometry of finite mixtures." Transactions of the American Mathematical Society 366, no. 2 (2013): 1047–72. http://dx.doi.org/10.1090/s0002-9947-2013-06041-2.

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11

Nagy, I., E. Suzdaleva, M. Kárný, and T. Mlynářová. "Bayesian estimation of dynamic finite mixtures." International Journal of Adaptive Control and Signal Processing 25, no. 9 (2011): 765–87. http://dx.doi.org/10.1002/acs.1239.

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12

Alfò, Marco, and Cecilia Vitiello. "Finite mixtures approach to ecological regression." Statistical Methods & Applications 12, no. 1 (2003): 93–108. http://dx.doi.org/10.1007/bf02511586.

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13

Sankaran, P. G., and Maya T. Nair. "On a Finite Mixture of Pareto Distributions." Calcutta Statistical Association Bulletin 57, no. 1-2 (2005): 67–84. http://dx.doi.org/10.1177/0008068320050106.

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Abstract (sommario):

Finite mixtures of probability distributions arise as models of income and wealth in many practical problems. In the present paper, we study the properties of finite mixture of Pareto distributions in the context of income analysis. We develop estimation of the parameters of the finite mixture of Pareto distributions using different methods for complete as well as censored samples. A simulation study and a data analysis are carried out to assess the performance of the estimators.

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14

Carreira-Perpiñán, Miguel Á., and Steve Renals. "Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions." Neural Computation 12, no. 1 (2000): 141–52. http://dx.doi.org/10.1162/089976600300015925.

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The class of finite mixtures of multivariate Bernoulli distributions is known to be nonidentifiable; that is, different values of the mixture parameters can correspond to exactly the same probability distribution. In principle, this would mean that sample estimates using this model would give rise to different interpretations. We give empirical support to the fact that estimation of this class of mixtures can still produce meaningful results in practice, thus lessening the importance of the identifiability problem. We also show that the expectation-maximization algorithm is guaranteed to conve

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15

Zhao, Yanjing, Jiwang Jiang, Yiqing Dai, Lan Zhou, and Fujian Ni. "Thermal Property Evaluation of Porous Asphalt Concrete Based on Heterogeneous Meso-Structure Finite Element Simulation." Applied Sciences 10, no. 5 (2020): 1671. http://dx.doi.org/10.3390/app10051671.

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Porous asphalt concrete (PAC) can obviously improve vehicle driving safety on rainy days and reduce environmental noise. It has been widely used in China. The existence of a large number of interconnected voids in PAC makes a significant difference in heat transfer and temperature distribution from conventional dense-graded asphalt concretes (AC). In this paper, the internal structure images of three dense-graded asphalt mixtures and one PAC were obtained by X-ray CT scanning technology, and the internal meso-structure finite element simulation models of asphalt mixtures were established by us

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16

Wang, Wan-Lun, Ahad Jamalizadeh, and Tsung-I. Lin. "Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions." Statistical Papers 61, no. 6 (2018): 2643–70. http://dx.doi.org/10.1007/s00362-018-01061-z.

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17

Haughton, Dominique. "Packages for Estimating Finite Mixtures: A Review." American Statistician 51, no. 2 (1997): 194. http://dx.doi.org/10.2307/2685419.

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18

Shi, Hualin, and S. T. Chui. "Droplets of mixtures of – at finite vorticity." Physica B: Condensed Matter 329-333 (May 2003): 166–67. http://dx.doi.org/10.1016/s0921-4526(02)01926-9.

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19

Gallaugher, Michael P. B., and Paul D. McNicholas. "Finite mixtures of skewed matrix variate distributions." Pattern Recognition 80 (August 2018): 83–93. http://dx.doi.org/10.1016/j.patcog.2018.02.025.

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20

Haughton, Dominique. "Packages for Estimating Finite Mixtures: A Review." American Statistician 51, no. 2 (1997): 194–205. http://dx.doi.org/10.1080/00031305.1997.10473961.

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21

HOLZMANN, HAJO, AXEL MUNK, and TILMANN GNEITING. "Identifiability of Finite Mixtures of Elliptical Distributions." Scandinavian Journal of Statistics 33, no. 4 (2006): 753–63. http://dx.doi.org/10.1111/j.1467-9469.2006.00505.x.

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22

Van Schaeybroeck, Bert. "Weakly interacting Bose mixtures at finite temperature." Physica A: Statistical Mechanics and its Applications 392, no. 17 (2013): 3806–11. http://dx.doi.org/10.1016/j.physa.2013.04.026.

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23

Karlis, Dimitris, Purushottam Papatla, and Sudipt Roy. "Finite mixtures of censored Poisson regression models." Statistica Neerlandica 70, no. 2 (2015): 100–122. http://dx.doi.org/10.1111/stan.12079.

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24

Rahman, Mezbahur, Rumanur Rahman, and Larry M. Pearson. "Quantiles for finite mixtures of normal distributions." International Journal of Mathematical Education in Science and Technology 37, no. 3 (2006): 352–58. http://dx.doi.org/10.1080/00207390500433103.

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25

Farrell, Patrick J., A. K. Md Ehsanes Saleh, and Zhengmin Zhang. "Methods of moments estimation in finite mixtures." Sankhya A 73, no. 2 (2011): 218–30. http://dx.doi.org/10.1007/s13171-011-0011-3.

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26

Chee, Chew-Seng, and Yong Wang. "Estimation of finite mixtures with symmetric components." Statistics and Computing 23, no. 2 (2011): 233–49. http://dx.doi.org/10.1007/s11222-011-9305-5.

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27

Yung, Yiu-Fai. "Finite mixtures in confirmatory factor-analysis models." Psychometrika 62, no. 3 (1997): 297–330. http://dx.doi.org/10.1007/bf02294554.

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28

Bermúdez, Lluís, Dimitris Karlis, and Isabel Morillo. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models." Risks 8, no. 1 (2020): 10. http://dx.doi.org/10.3390/risks8010010.

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Abstract (sommario):

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical re

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29

Maleki, Mohsen, Javier Contreras-Reyes, and Mohammad Mahmoudi. "Robust Mixture Modeling Based on Two-Piece Scale Mixtures of Normal Family." Axioms 8, no. 2 (2019): 38. http://dx.doi.org/10.3390/axioms8020038.

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In this paper, we examine the finite mixture (FM) model with a flexible class of two-piece distributions based on the scale mixtures of normal (TP-SMN) family components. This family allows the development of a robust estimation of FM models. The TP-SMN is a rich class of distributions that covers symmetric/asymmetric and light/heavy tailed distributions. It represents an alternative family to the well-known scale mixtures of the skew normal (SMSN) family studied by Branco and Dey (2001). Also, the TP-SMN covers the SMN (normal, t, slash, and contaminated normal distributions) as the symmetric

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30

Martín, Juan Carlos. "Analysis of the complex effective permittivity of a heterogeneous sample by the finite-difference time-domain method." Canadian Journal of Physics 87, no. 4 (2009): 337–43. http://dx.doi.org/10.1139/p09-028.

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Numerical results of the complex effective permittivity and DC conductivity of heterogeneous samples with random constituents’ distribution are analyzed to search for a suitable mixture law. The calculation method consists of the simulation of a time-domain reflectometry experiment by means of the finite-difference time-domain (FDTD) algorithm. The permittivity and conductivity of several series of samples with their constituents distributed at random are calculated. Numerical results, as a function of the volume fractions of the mixture’s constituents, are compared with analytical mixture law

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31

Al-Moisheer, A. S. "Sequential Test for a Mixture of Finite Exponential Distribution." Journal of Mathematics 2021 (April 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625853.

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Testing the number of components in a finite mixture is considered one of the challenging problems. In this paper, exponential finite mixtures are used to determine the number of components in a finite mixture. A sequential testing procedure is adopted based on the likelihood ratio test (LRT) statistic. The distribution of the test statistic under the null hypothesis is obtained using a resampling technique based on B bootstrap samples. The quantiles of the distribution of the test statistic are evaluated from the B bootstrap samples. The performance of the test is examined through the empiric

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32

SETIAWATY, B. "A SURVEY ON THE IDENTIFIABILITY OF FINITE MIXTURES." Journal of Mathematics and Its Applications 3, no. 2 (2004): 29. http://dx.doi.org/10.29244/jmap.3.2.29-44.

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This paper is a survey on the identiﬁability of ﬁnite mixtures. We collect all the results regarding suﬃcient conditions for ﬁnite mixtures to be identiﬁable and what kind of distributions family which is identiﬁable.

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33

Frühwirth-Schnatter, Sylvia, and Saumyadipta Pyne. "Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions." Biostatistics 11, no. 2 (2010): 317–36. http://dx.doi.org/10.1093/biostatistics/kxp062.

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Abstract Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on da

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34

Goncharenko, M. B., and T. V. Zakharova. "Specific Features of Finite Mixtures of Normal Distributions." Moscow University Computational Mathematics and Cybernetics 42, no. 3 (2018): 126–32. http://dx.doi.org/10.3103/s0278641918030068.

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35

Karlis, Dimitris, and Loukia Meligkotsidou. "Finite mixtures of multivariate Poisson distributions with application." Journal of Statistical Planning and Inference 137, no. 6 (2007): 1942–60. http://dx.doi.org/10.1016/j.jspi.2006.07.001.

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36

Datta, Gauri Sankar. "On symmetry of finite mixtures of normal distributions." Journal of Statistical Planning and Inference 137, no. 9 (2007): 2993–95. http://dx.doi.org/10.1016/j.jspi.2006.11.003.

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37

Tashman, Adam, and Robert J. Frey. "Modeling risk in arbitrage strategies using finite mixtures§." Quantitative Finance 9, no. 5 (2009): 495–503. http://dx.doi.org/10.1080/14697680802595635.

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38

Steiner, Ullrich, Jacob Klein, and Lewis J. Fetters. "Surface phase inversion in finite-sized binary mixtures." Physical Review Letters 72, no. 10 (1994): 1498–501. http://dx.doi.org/10.1103/physrevlett.72.1498.

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39

ARMINGER, GERHARD, and PETRA STEIN. "Finite Mixtures of Covariance Structure Models with Regressors." Sociological Methods & Research 26, no. 2 (1997): 148–82. http://dx.doi.org/10.1177/0049124197026002002.

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40

Dattatreya, G. R., and L. N. Kanal. "Estimation of mixing probabilities in multiclass finite mixtures." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 1 (1990): 149–58. http://dx.doi.org/10.1109/21.47817.

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41

Hanson, Timothy E. "Inference for Mixtures of Finite Polya Tree Models." Journal of the American Statistical Association 101, no. 476 (2006): 1548–65. http://dx.doi.org/10.1198/016214506000000384.

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42

Ishwaran, Hemant, and Lancelot F. James. "Approximate Dirichlet Process Computing in Finite Normal Mixtures." Journal of Computational and Graphical Statistics 11, no. 3 (2002): 508–32. http://dx.doi.org/10.1198/106186002411.

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43

Henry, Marc, Yuichi Kitamura, and Bernard Salanié. "Partial identification of finite mixtures in econometric models." Quantitative Economics 5, no. 1 (2014): 123–44. http://dx.doi.org/10.3982/qe170.

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44

Tsionas, Efthymios G. "BAYESIAN ANALYSIS OF FINITE MIXTURES OF WEIBULL DISTRIBUTIONS." Communications in Statistics - Theory and Methods 31, no. 1 (2002): 37–48. http://dx.doi.org/10.1081/sta-120002433.

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45

Craigmile, Peter F., and D. M. Tirrerington. "Parameter estimation for finite mixtures of uniform distributions." Communications in Statistics - Theory and Methods 26, no. 8 (1997): 1981–95. http://dx.doi.org/10.1080/03610929708832026.

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46

Jones, P. N., and G. J. Mc Lachlan. "Modelling mass-size particle data by finite mixtures." Communications in Statistics - Theory and Methods 18, no. 7 (1989): 2629–46. http://dx.doi.org/10.1080/03610928908830054.

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47

Thomas, Hoben, and Thomas P. Hettmansperger. "Modelling change in cognitive understanding with finite mixtures." Journal of the Royal Statistical Society: Series C (Applied Statistics) 50, no. 4 (2001): 435–48. http://dx.doi.org/10.1111/1467-9876.00246.

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48

Depraetere, Nicolas, and Martina Vandebroek. "Order selection in finite mixtures of linear regressions." Statistical Papers 55, no. 3 (2013): 871–911. http://dx.doi.org/10.1007/s00362-013-0534-x.

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49

Follmann, Dean A., and Diane Lambert. "Identifiability of finite mixtures of logistic regression models." Journal of Statistical Planning and Inference 27, no. 3 (1991): 375–81. http://dx.doi.org/10.1016/0378-3758(91)90050-o.

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50

Yang, Xiangyu, and Shankar M. Krishnan. "Image segmentation using finite mixtures and spatial information." Image and Vision Computing 22, no. 9 (2004): 735–45. http://dx.doi.org/10.1016/j.imavis.2004.04.003.

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