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Journal articles on the topic 'Mixture models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Razzaghi, Mehdi, Geoffrey J. McLachan, and Kaye E. Basford. "Mixture Models." Technometrics 33, no. 3 (1991): 365. http://dx.doi.org/10.2307/1268796.

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2

Razraghi, Mehdi. "Mixture Models." Technometrics 33, no. 3 (1991): 365–66. http://dx.doi.org/10.1080/00401706.1991.10484850.

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3

Ueda, Naonori, Ryohei Nakano, Zoubin Ghahramani, and Geoffrey E. Hinton. "SMEM Algorithm for Mixture Models." Neural Computation 12, no. 9 (2000): 2109–28. http://dx.doi.org/10.1162/089976600300015088.

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We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mi

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4

Achcar, Jorge A., Emílio A. Coelho-Barros, and Josmar Mazucheli. "Cure fraction models using mixture and non-mixture models." Tatra Mountains Mathematical Publications 51, no. 1 (2012): 1–9. http://dx.doi.org/10.2478/v10127-012-0001-4.

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ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

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5

Le, Si Quang, Nicolas Lartillot, and Olivier Gascuel. "Phylogenetic mixture models for proteins." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (2008): 3965–76. http://dx.doi.org/10.1098/rstb.2008.0180.

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Standard protein substitution models use a single amino acid replacement rate matrix that summarizes the biological, chemical and physical properties of amino acids. However, site evolution is highly heterogeneous and depends on many factors: genetic code; solvent exposure; secondary and tertiary structure; protein function; etc. These impact the substitution pattern and, in most cases, a single replacement matrix is not enough to represent all the complexity of the evolutionary processes. This paper explores in maximum-likelihood framework phylogenetic mixture models that combine several amin

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6

McLachlan, Geoffrey J., Sharon X. Lee, and Suren I. Rathnayake. "Finite Mixture Models." Annual Review of Statistics and Its Application 6, no. 1 (2019): 355–78. http://dx.doi.org/10.1146/annurev-statistics-031017-100325.

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The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications w

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7

Shanmugam, Ramalingam. "Finite Mixture Models." Technometrics 44, no. 1 (2002): 82. http://dx.doi.org/10.1198/tech.2002.s651.

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8

Verbeek, J. J., N. Vlassis, and B. Kröse. "Efficient Greedy Learning of Gaussian Mixture Models." Neural Computation 15, no. 2 (2003): 469–85. http://dx.doi.org/10.1162/089976603762553004.

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This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points an

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9

Nemec, James M., and Amanda F. L. Nemec. "Mixture models for studying stellar populations. II - Multivariate finite mixture models." Astronomical Journal 105 (April 1993): 1455. http://dx.doi.org/10.1086/116523.

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10

Focke, Walter W. "Mixture Models Based on Neural Network Averaging." Neural Computation 18, no. 1 (2006): 1–9. http://dx.doi.org/10.1162/089976606774841576.

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A modified version of the single hidden-layer perceptron architecture is proposed for modeling mixtures. A particular flexible mixture model is obtained by implementing the Box-Cox transformation as transfer function. In this case, the network response can be expressed in closed form as a weighted power mean. The quadratic Scheffé K-polynomial and the exponential Wilson equation turn out to be special forms of this general mixture model. Advantages of the proposed network architecture are that binary data sets suffice for “training” and that it is readily extended to incorporate additional mix

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11

Hettinger, Thomas, and Marion Frank. "Stochastic and Temporal Models of Olfactory Perception." Chemosensors 6, no. 4 (2018): 44. http://dx.doi.org/10.3390/chemosensors6040044.

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Olfactory systems typically process signals produced by mixtures composed of very many natural odors, some that can be elicited by single compounds. The several hundred different olfactory receptors aided by several dozen different taste receptors are sufficient to define our complex chemosensory world. However, sensory processing by selective adaptation and mixture suppression leaves only a few perceptual components recognized at any time. Thresholds determined by stochastic processes are described by functions relating stimulus detection to concentration. Relative saliences of mixture compon

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12

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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13

Chisala, Maxwell. "Cement Concrete Mixture Performance Characterization." Budownictwo i Architektura 17, no. 4 (2019): 103–20. http://dx.doi.org/10.24358/bud-arch_18_174_10.

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The cementitious composite nature of concrete makes very diffi cult directly ascertaining each mixture-factors’ contribution to a given concrete mixture performance characteristics but also doubly diffi cult to accurately balance mutually exclusive requirements for performance (workability, strength, durability) and sustainability (the economic and effi cient use of materials) for mixture proportioning based on recipes of previously produced concretes. This study sought to quantify individual mixture-factors’ contribution to a given concrete mixture’s performance characteristics. Proposed mult

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14

Chen, Jiahua. "On finite mixture models." Statistical Theory and Related Fields 1, no. 1 (2017): 15–27. http://dx.doi.org/10.1080/24754269.2017.1321883.

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15

Lindsay, Bruce G. "Discussion: Semiparametric mixture models." Journal of Nonparametric Statistics 1, no. 1-2 (1991): 51–55. http://dx.doi.org/10.1080/10485259108832508.

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16

Ju, Zhaojie, and Honghai Liu. "Fuzzy Gaussian Mixture Models." Pattern Recognition 45, no. 3 (2012): 1146–58. http://dx.doi.org/10.1016/j.patcog.2011.08.028.

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17

Nguyen, Hien D., Geoffrey J. McLachlan, Jeremy F. P. Ullmann, and Andrew L. Janke. "Laplace mixture autoregressive models." Statistics & Probability Letters 110 (March 2016): 18–24. http://dx.doi.org/10.1016/j.spl.2015.11.006.

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18

McNicholas, Paul David, and Thomas Brendan Murphy. "Parsimonious Gaussian mixture models." Statistics and Computing 18, no. 3 (2008): 285–96. http://dx.doi.org/10.1007/s11222-008-9056-0.

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19

Kalli, Maria, Jim E. Griffin, and Stephen G. Walker. "Slice sampling mixture models." Statistics and Computing 21, no. 1 (2009): 93–105. http://dx.doi.org/10.1007/s11222-009-9150-y.

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20

Viroli, Cinzia, and Geoffrey J. McLachlan. "Deep Gaussian mixture models." Statistics and Computing 29, no. 1 (2017): 43–51. http://dx.doi.org/10.1007/s11222-017-9793-z.

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21

Bunea, Florentina, Alexandre B. Tsybakov, Marten H. Wegkamp, and Adrian Barbu. "SPADES and mixture models." Annals of Statistics 38, no. 4 (2010): 2525–58. http://dx.doi.org/10.1214/09-aos790.

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22

Verbeek, J. J., N. Vlassis, and B. J. A. Kröse. "Self-organizing mixture models." Neurocomputing 63 (January 2005): 99–123. http://dx.doi.org/10.1016/j.neucom.2004.04.008.

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23

Varriale, Roberta, and Jeroen K. Vermunt. "Multilevel Mixture Factor Models." Multivariate Behavioral Research 47, no. 2 (2012): 247–75. http://dx.doi.org/10.1080/00273171.2012.658337.

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24

Marriott, Paul. "Extending local mixture models." Annals of the Institute of Statistical Mathematics 59, no. 1 (2007): 95–110. http://dx.doi.org/10.1007/s10463-006-0100-6.

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25

Böhning, Dankmar, Wilfried Seidel, Macro Alfó, Bernard Garel, Valentin Patilea, and Günther Walther. "Advances in Mixture Models." Computational Statistics & Data Analysis 51, no. 11 (2007): 5205–10. http://dx.doi.org/10.1016/j.csda.2006.10.025.

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26

Broda, Simon A., Markus Haas, Jochen Krause, Marc S. Paolella, and Sven C. Steude. "Stable mixture GARCH models." Journal of Econometrics 172, no. 2 (2013): 292–306. http://dx.doi.org/10.1016/j.jeconom.2012.08.012.

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27

Alman, David H., and Charles G. Pfeifer. "Empirical colorant mixture models." Color Research & Application 12, no. 4 (1987): 210–22. http://dx.doi.org/10.1002/col.5080120409.

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28

Rosseel, Yves. "Mixture Models of Categorization." Journal of Mathematical Psychology 46, no. 2 (2002): 178–210. http://dx.doi.org/10.1006/jmps.2001.1379.

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29

von Davier, Matthias. "MIXTURE DISTRIBUTION DIAGNOSTIC MODELS." ETS Research Report Series 2007, no. 2 (2007): i—21. http://dx.doi.org/10.1002/j.2333-8504.2007.tb02074.x.

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30

Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (2021): 124. http://dx.doi.org/10.3390/nano11010124.

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Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational

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31

Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (2021): 124. http://dx.doi.org/10.3390/nano11010124.

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Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational

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32

Long, Wu Jian, Kamal Henri Khayat, and Feng Xing. "Statistical Models to Predict Fresh Properties of Self-Consolidating Concrete." Advanced Materials Research 129-131 (August 2010): 853–56. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.853.

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In order to understand the influence of mixture parameters on concrete behaviour, a factorial design was employed in this investigation to identify the relative significance of primary mixture parameters and their coupled effects (interactions) on fresh properties of SCC that are of special interest to precast, prestressed applications. In addition to the 16 SCC mixtures employed, three SCC mixtures corresponding to the central point of the factorial design were prepared to estimate the degree of the experimental error for each of the modeled responses. The mixtures were evaluated to determine

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33

YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of

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34

Tham, Mun Wai, MR Nurul Fazita, HPS Abdul Khalil, et al. "Tensile properties prediction of natural fibre composites using rule of mixtures: A review." Journal of Reinforced Plastics and Composites 38, no. 5 (2018): 211–48. http://dx.doi.org/10.1177/0731684418813650.

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Rule of mixture models are usually used in the tensile properties prediction of polymer composites reinforced with synthetic fibres. They are less utilized for natural fibre/polymer composites due to natural fibres physical and mechanical properties variability which reduces rule of mixture model's prediction values accuracy compared to the experimental values. This had led to studies conducted by various researchers to improve the existing rule of mixture models to give a better reflection of the true natural fibres properties and enhance the rule of mixture models prediction accuracy. In thi

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35

Khuri, André I. "Slack-variable models versus Scheffé's mixture models." Journal of Applied Statistics 32, no. 9 (2005): 887–908. http://dx.doi.org/10.1080/02664760500163466.

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36

Huber, Gerald A., Xishun Zhang, and Robin Fontaine. "Superpave Models: Predicting Performance during Design and Construction." Transportation Research Record: Journal of the Transportation Research Board 1545, no. 1 (1996): 105–12. http://dx.doi.org/10.1177/0361198196154500114.

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The Strategic Highway Research Program (SHRP) spent $50 million researching asphalt binders and asphalt mixtures and provided three main products: an asphalt binder specification, an asphalt mixture specification, and Superpave, an asphalt mixture design system that encompasses both the binder and mixture specification. SHRP researchers have provided tools that promise more robust asphalt mixtures with reduced risk of premature failure. Implementation of the specifications and mix design system will require overcoming several obstacles. Superpave must be demonstrated to be practical and easy t

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37

Abendroth, Julie A., Erin E. Blankenship, Alex R. Martin, and Fred W. Roeth. "Joint Action Analysis Utilizing Concentration Addition and Independent Action Models." Weed Technology 25, no. 3 (2011): 436–46. http://dx.doi.org/10.1614/wt-d-10-00102.1.

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In weed science literature, models such as concentration addition, independent action, effect summation, and the parallel line assay technique have been used to predict and analyze whole-plant response to herbicide mixtures. Although a joint action reference model is necessary for determining whether the herbicide mixture provides less than (antagonistic), equal to (zero-interaction or additive), or greater than (synergistic) expected control, model selection often occurs with little regard to the model's underlying biological assumptions. The joint action models of concentration addition (CA)

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38

Bather, J. A. "Search models." Journal of Applied Probability 29, no. 3 (1992): 605–15. http://dx.doi.org/10.2307/3214897.

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Mathematical models have been proposed for oil exploration and other kinds of search. They can be used to estimate the amount of undiscovered resources or to investigate optimal stopping times for the search. Here we consider a continuous search for hidden objects using a model which represents the number and values of the objects by mixtures of Poisson processes. The flexibility of the model and its complexity depend on the number of components in the mixture. In simple cases, optimal stopping rules can be found explicitly and more general qualitative results can sometimes be obtained.

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39

Bather, J. A. "Search models." Journal of Applied Probability 29, no. 03 (1992): 605–15. http://dx.doi.org/10.1017/s0021900200043424.

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Mathematical models have been proposed for oil exploration and other kinds of search. They can be used to estimate the amount of undiscovered resources or to investigate optimal stopping times for the search. Here we consider a continuous search for hidden objects using a model which represents the number and values of the objects by mixtures of Poisson processes. The flexibility of the model and its complexity depend on the number of components in the mixture. In simple cases, optimal stopping rules can be found explicitly and more general qualitative results can sometimes be obtained.

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40

Maleki, Mohsen, and A. R. Nematollahi. "Autoregressive Models with Mixture of Scale Mixtures of Gaussian Innovations." Iranian Journal of Science and Technology, Transactions A: Science 41, no. 4 (2017): 1099–107. http://dx.doi.org/10.1007/s40995-017-0237-6.

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41

Zakirova, Gulnur, Vladimir Pshenin, Radmir Tashbulatov, and Lyubov Rozanova. "Modern Bitumen Oil Mixture Models in Ashalchinsky Field with Low-Viscosity Solvent at Various Temperatures and Solvent Concentrations." Energies 16, no. 1 (2022): 395. http://dx.doi.org/10.3390/en16010395.

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The article analyzes the modern theory and practice of pipeline transport of bituminous oil together with low-viscosity solvent. In addition, a detailed analysis of the rheological models of non-Newtonian fluids is carried out, which establishes a number of assumptions on the rheology model selection algorithm currently in use (limited number of rheological models, variability in model coefficient assignment, etc.). Ways of their elimination are proposed. Dependencies for determination of the dynamic viscosity coefficient of binary oil mixtures are investigated. Calculation of the parameters o

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42

Knezevic-Stevanovic, Andjela, Goran Babic, Mirjana Kijevcanin, Slobodan Serbanovic, and Dusan Grozdanic. "Liquid mixture viscosities correlation with rational models." Journal of the Serbian Chemical Society 79, no. 3 (2014): 341–44. http://dx.doi.org/10.2298/jsc130610114k.

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In this paper twenty two selected rational correlation models for liquid mixture viscosities of organic compounds were tested on 219 binary sets of experimental data taken from literature. The binary sets contained 3675 experimental data points for 70 different compounds. The Dimitrov-Kamenski X, Dimitrov-Kamenski XII, and Dimitrov-Kamenski XIII models demonstrated the best correlative characteristics for binary mixtures with overall absolute average deviation less then 2%.

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43

Gu, Jiaying, Roger Koenker, and Stanislav Volgushev. "TESTING FOR HOMOGENEITY IN MIXTURE MODELS." Econometric Theory 34, no. 4 (2017): 850–95. http://dx.doi.org/10.1017/s0266466617000299.

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Statistical models of unobserved heterogeneity are typically formalized as mixtures of simple parametric models and interest naturally focuses on testing for homogeneity versus general mixture alternatives. Many tests of this type can be interpreted as C(α) tests, as in Neyman (1959), and shown to be locally asymptotically optimal. These C(α) tests will be contrasted with a new approach to likelihood ratio testing for general mixture models. The latter tests are based on estimation of general nonparametric mixing distribution with the Kiefer and Wolfowitz (1956) maximum likelihood estimator. R

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44

Ivanov, L. A., L. D. Xu, E. S. Bokova, A. D. Ishkov, and S. R. Muminova. "Nanotechnologies: a review of inventions and utility models. Part V." Nanotechnologies in Construction A Scientific Internet-Journal 12, no. 6 (2020): 331–38. http://dx.doi.org/10.15828/2075-8545-2020-12-6-331-338.

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The article provides an abstract review of patents. The results of creative activity of scientists, engineers and specialists, including inventions in the field of nanotechnology and nanomaterials, being implemented, allow achieving a significant effect in construction, housing and community services, and related sectors of the economy. For example, the invention «A method to produce dry construction mixtures» refers to manufacturing of building materials, in particularly, to manufacture of dry construction mixtures (DCM) by the method of joint mechanoactivation of cement and dolomite, with fu

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45

Campbell, Joanna Tochman, and Maria L. Weese. "Compositional Models and Organizational Research." Organizational Research Methods 20, no. 1 (2016): 95–120. http://dx.doi.org/10.1177/1094428116672002.

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An emergent stream of research in management employs configurational and holistic approaches to understanding macro and micro phenomena. In this study, we introduce mixture models—a related class of models—to organizational research and show how they can be applied to nonexperimental data. Specifically, we reexamine the long-standing research question concerning the CEO pay–firm performance relationship using a novel empirical approach, treating individual pay elements as components of a mixture, and demonstrate its utility for other research questions involving mixtures or proportions. Throug

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46

Bassetti, Federico, and Lucia Ladelli. "Mixture of Species Sampling Models." Mathematics 9, no. 23 (2021): 3127. http://dx.doi.org/10.3390/math9233127.

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We introduce mixtures of species sampling sequences (mSSS) and discuss how these sequences are related to various types of Bayesian models. As a particular case, we recover species sampling sequences with general (not necessarily diffuse) base measures. These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). Extending previous results, we prove that mSSS are obtained by assigning the values of an e

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47

Okechi, Reuben N., Oluchukwu R. Nwangwu, Christian C. Opurum, and Emmanuel C. Nleonu. "Synergistic toxicities of binary and ternary mixtures of an anionic surfactant and divalent metals to Lysinibacillus fusiformis isolated from a vegetable farm." Journal of Toxicological Studies 3, no. 1 (2024): 1658. https://doi.org/10.59400/jts1658.

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The toxicities of the heavy metals (Pb, Cd, Ni, Zn, and Co) and their ternary mixtures with Sodium Dodecyl Sulfate (SDS) to Lysinibacillus fusiformis isolated from Talinum fruticosum farms irrigated with Otamiri River water in Owerri, Imo State, Nigeria, were assessed using dehydrogenase activity (DHA) restriction as an endpoint. Fixed ratio mixtures (arbitrary concentration ratio (ABCR) and equi-effect concentration ratio (EECR) mixtures) were formulated to evaluate the combined toxicities of these toxicants. Toxicities were predicted with concentration addition (CA) and independent action (I

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48

Ansari, Zoe, Adriano Agnello, and Christa Gall. "Mixture models for photometric redshifts." Astronomy & Astrophysics 650 (June 2021): A90. http://dx.doi.org/10.1051/0004-6361/202039675.

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Context. Determining photometric redshifts (photo-zs) of extragalactic sources to a high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo-zs are prone to systematic uncertainties in the intervening extinction and the unknown underlying spectral-energy distribution of different astrophysical sources, leading to degeneracies in the modern machine learning algorithm that impacts the level of accuracy for photo-z estimates. Aims. Here, we aim to resolve these model degeneracies and obtain a clear separation between

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49

Jalali, Assad, and John Pemberton. "Mixture models for time series." Journal of Applied Probability 32, no. 1 (1995): 123–38. http://dx.doi.org/10.2307/3214925.

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In this paper we extend the class of zero-order threshold autoregressive models to a much richer class of mixture models. The new class has the important property of duality which, as we show, corresponds to time reversal. We are then able to obtain the time reversals of the zero-order threshold models and to characterise the time-reversible members of this subclass. These turn out to be quite trivial. The time-reversible models of the more general class do not suffer in this way. The complete stationary distributional structure is given, as are various moments, in particular the autocovarianc

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50

Lesperance, Mary L., and John D. Kalbfleisch. "Mixture models for matched pairs." Canadian Journal of Statistics 22, no. 1 (1994): 65–74. http://dx.doi.org/10.2307/3315823.

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