Journal articles: 'Mixture models'

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

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

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Ueda, Naonori, Ryohei Nakano, Zoubin Ghahramani, and Geoffrey E. Hinton. "SMEM Algorithm for Mixture Models." Neural Computation 12, no. 9 (September 1, 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 mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split- and-merge operations to improve the likelihood of both the training data and of held-out test data. We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems.

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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 (November 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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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 (October 7, 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 amino acid replacement matrices to better fit protein evolution. We learn these mixture models from a large alignment database extracted from HSSP, and test the performance using independent alignments from TreeBase . We compare unsupervised learning approaches, where the site categories are unknown, to supervised ones, where in estimations we use the known category of each site, based on its exposure or its secondary structure. All our models are combined with gamma-distributed rates across sites. Results show that highly significant likelihood gains are obtained when using mixture models compared with the best available single replacement matrices. Mixtures of matrices also improve over mixtures of profiles in the manner of the CAT model. The unsupervised approach tends to be better than the supervised one, but it appears difficult to implement and highly sensitive to the starting values of the parameters, meaning that the supervised approach is still of interest for initialization and model comparison. Using an unsupervised model involving three matrices, the average AIC gain per site with TreeBase test alignments is 0.31, 0.49 and 0.61 compared with LG (named after Le & Gascuel 2008 Mol. Biol. Evol. 25 , 1307–1320), WAG and JTT, respectively. This three-matrix model is significantly better than LG for 34 alignments (among 57), and significantly worse for 1 alignment only. Moreover, tree topologies inferred with our mixture models frequently differ from those obtained with single matrices, indicating that using these mixtures impacts not only the likelihood value but also the output tree. All our models and a PhyML implementation are available from http://atgc.lirmm.fr/mixtures .

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McLachlan, Geoffrey J., Sharon X. Lee, and Suren I. Rathnayake. "Finite Mixture Models." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 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 where there is group-structure in the data or where the aim is to explore the data for such structure, as in a cluster analysis. It has now been three decades since the publication of the monograph by McLachlan & Basford (1988) with an emphasis on the potential usefulness of mixture models for inference and clustering. Since then, mixture models have attracted the interest of many researchers and have found many new and interesting fields of application. Thus, the literature on mixture models has expanded enormously, and as a consequence, the bibliography here can only provide selected coverage.

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

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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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Verbeek, J. J., N. Vlassis, and B. Kröse. "Efficient Greedy Learning of Gaussian Mixture Models." Neural Computation 15, no. 2 (February 1, 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 and quadratic in the (final) number of mixture components. Due to its greedy nature, the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation are provided.

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Focke, Walter W. "Mixture Models Based on Neural Network Averaging." Neural Computation 18, no. 1 (January 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 mixture components while retaining all previously determined weights.

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Chen, Jiahua. "On finite mixture models." Statistical Theory and Related Fields 1, no. 1 (January 2, 2017): 15–27. http://dx.doi.org/10.1080/24754269.2017.1321883.

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

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

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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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McNicholas, Paul David, and Thomas Brendan Murphy. "Parsimonious Gaussian mixture models." Statistics and Computing 18, no. 3 (April 19, 2008): 285–96. http://dx.doi.org/10.1007/s11222-008-9056-0.

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Kalli, Maria, Jim E. Griffin, and Stephen G. Walker. "Slice sampling mixture models." Statistics and Computing 21, no. 1 (September 19, 2009): 93–105. http://dx.doi.org/10.1007/s11222-009-9150-y.

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

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

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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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Varriale, Roberta, and Jeroen K. Vermunt. "Multilevel Mixture Factor Models." Multivariate Behavioral Research 47, no. 2 (March 30, 2012): 247–75. http://dx.doi.org/10.1080/00273171.2012.658337.

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Marriott, Paul. "Extending local mixture models." Annals of the Institute of Statistical Mathematics 59, no. 1 (February 7, 2007): 95–110. http://dx.doi.org/10.1007/s10463-006-0100-6.

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

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

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

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

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

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Tzougas, George, Spyridon Vrontos, and Nicholas Frangos. "OPTIMAL BONUS-MALUS SYSTEMS USING FINITE MIXTURE MODELS." ASTIN Bulletin 44, no. 2 (January 10, 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 finite Exponential, Gamma, Weibull and Generalized Beta Type II mixtures, updating the posterior probability. We also consider the case of a finite Negative Binomial mixture and a finite Pareto mixture updating the posterior mean. The generalized Bonus-Malus Systems we propose, integrate risk classification and experience rating by taking into account both the a priori and a posteriori characteristics of each policyholder.

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Hettinger, Thomas, and Marion Frank. "Stochastic and Temporal Models of Olfactory Perception." Chemosensors 6, no. 4 (September 26, 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 components are established by relating component recognition to concentration in the presence of background components. Mathematically distinct stochastic models of perceptual component dominance in binary mixtures were developed that accommodate prediction of an appropriate range of probabilities from 0 to 1, and include errors in identifications. Prior short-term selective adaptation to some components allows temporally emergent recognition of non-adapted mixture-suppressed components. Thus, broadly tuned receptors are neutralized or suppressed by activation of other more efficacious receptors. This ‘combinatorial’ coding is more a process of subtraction than addition, with the more intense components dominating the perception. It is in this way that complex chemosensory mixtures are reduced to manageable numbers of odor notes and taste qualities.

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

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Chisala, Maxwell. "Cement Concrete Mixture Performance Characterization." Budownictwo i Architektura 17, no. 4 (February 28, 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 multi-parametric exponential mixture-response models were fi tted to available test-performance data sets of HPC mixtures proportioned based on the best combined grade aggregate (minimum void) to generate mixture-strength and mixture-porosity development (age-mixture response relationships) profi les of HPC mixtures and deemed robust enough to yield reliable determination of mixture-response rate-parameters So, Sp, Si and Po, Pp, Pi as functions of mixture-factors that permitted reliable quantifi cation of contributions to HPC mixture performance of individual mixture-factors and optimization of mixture properties under study over the study domain. Mixture-response sensitivity analysis models (or mixture response trace plots) to allow construction of mixture-factor envelopes and ultimately optimized mixture-response models to facilitate selection of optimal mixture-factors and optimal tailoring of HPC mixture requirements to HPC mixture performance were developed and used to obtain optimized adapted HPC mixtures from available high performance concrete (HPC) mixture design recipes investigated in the study over the study domain. Adapted HPC mixture design recipes yielded alternative mixture compositions with improved performance and effi ciency characteristics with statistical performance metrics MAPE, NMBE and RMSE values of 7.6%,–3.7% and 6.5 MPa, respectively.

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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 several key responses that affect the fresh properties of precast, prestressed concrete, including filling ability, passing ability, filling capacity, surface settlement, and column segregation. Mixture parameters modeled in this investigation included the binder content, binder type, w/cm, sand-to-total aggregate ratio (S/A), and dosage of thickening-type, viscosity-modifying admixture (VMA). The factorial design can identify potential mixtures with a given set of performance criteria that can be tried in the laboratory, hence simplifying the test protocol needed to optimize SCC.

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Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (January 7, 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 models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

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Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (January 7, 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 models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

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Bather, J. A. "Search models." Journal of Applied Probability 29, no. 3 (September 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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Bather, J. A. "Search models." Journal of Applied Probability 29, no. 03 (September 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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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 (April 21, 2017): 1099–107. http://dx.doi.org/10.1007/s40995-017-0237-6.

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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 (January 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 to use. The impact of Superpave aggregate requirements on aggregate availability must be determined. The Superpave gyratory compaction procedure has been uniquely defined and then calibrated to traffic volume. The reasonableness of this approach must be tested in widespread application. Perhaps the largest implementation hurdle exists in the performance models. Expensive test equipment is necessary to do the performance-based tests. The performance predictions must be established as reasonable to justify the cost. A highway reconstruction project containing three Superpave Level 1 mix designs is documented including quality control done with the Superpave gyratory compactor. Superpave Level 2 performance-based tests were carried out to predict permanent deformation of the design and the mixture as constructed. The performance-based engineering properties obtained from the tests are evaluated, and the reasonableness of the performance prediction models is discussed.

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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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Gu, Jiaying, Roger Koenker, and Stanislav Volgushev. "TESTING FOR HOMOGENEITY IN MIXTURE MODELS." Econometric Theory 34, no. 4 (July 24, 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. Recent developments in convex optimization have dramatically improved upon earlier EM methods for computation of these estimators, and recent results on the large sample behavior of likelihood ratios involving such estimators yield a tractable form of asymptotic inference. Improvement in computation efficiency also facilitates the use of a bootstrap method to determine critical values that are shown to work better than the asymptotic critical values in finite samples. Consistency of the bootstrap procedure is also formally established. We compare performance of the two approaches identifying circumstances in which each is preferred.

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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 (September 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) and independent action (IA) are the appropriate models to consider for analysis of herbicide mixtures of two active components. CA assumes additivity of dose, with herbicides differing only in potency, whereas IA assumes multiplicativity of effects, in which herbicides behave independently and sequentially within the plant. CA and IA predicted mixture responses were computed for a sample mixture data set of mesotrione plus atrazine. IA predicted lower mixture responses than CA; for example, mesotrione at 17.5 g ha−1+ atrazine at 140 g ha−1was predicted to provide 45% (IA) compared with 53% (CA) control of Palmer amaranth. Joint action claims of synergism and antagonism were shown to be dependent on the reference model selected. Although mesotrione plus atrazine combinations were synergistic under IA assumptions, analysis under CA assumptions indicated mesotrione plus atrazine to be synergistic, additive, and antagonistic according to the selected effective concentration (ECx) level and fixed-ratio mixture. Because it is not possible to determine the appropriate joint action model on the basis of fit of predicted to observed mixture data, the appropriateness of underlying biological assumptions was considered for the sample mixture data set. Additionally, we provide decision criteria to aid researchers in their selection of an appropriate joint action reference model.

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YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (December 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 a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.

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Tham, Mun Wai, MR Nurul Fazita, HPS Abdul Khalil, Nurul Zuhairah Mahmud Zuhudi, Mariatti Jaafar, Samsul Rizal, and MK Mohamad Haafiz. "Tensile properties prediction of natural fibre composites using rule of mixtures: A review." Journal of Reinforced Plastics and Composites 38, no. 5 (November 26, 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 this paper, rule of mixture model's utilization includes the existing rule of mixture models as well as proposed rule of mixture models which have one or more factors incorporated into existing rule of mixture models for natural fibre/polymer composites tensile properties prediction are reviewed.

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Campbell, Joanna Tochman, and Maria L. Weese. "Compositional Models and Organizational Research." Organizational Research Methods 20, no. 1 (October 25, 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. Through this, we provide a step-by-step guide for other researchers interested in compositional modeling. Our results highlight that a more nuanced approach to understanding the influence of executive compensation on firm performance brings new insights to this research stream, showcasing the potential of compositional models for other literatures.

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Bassetti, Federico, and Lucia Ladelli. "Mixture of Species Sampling Models." Mathematics 9, no. 23 (December 4, 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 exchangeable sequence to the classes of a latent exchangeable random partition. Using this representation, we give an explicit expression of the Exchangeable Partition Probability Function of the partition generated by an mSSS. Some special cases are discussed in detail—in particular, species sampling sequences with general base measures and a mixture of species sampling sequences with Gibbs-type latent partition. Finally, we give explicit expressions of the predictive distributions of an mSSS.

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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 (December 29, 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 of the bituminous oil mixture with solvent is considered. Complex experimental studies on rheology mixture models of bituminous oil and solvent on the example of the Ashalchinsky field (Russia, Tatarstan) in a wide range of temperatures and concentrations of the solvent are conducted. A two-dimensional field of rheological models of the oil mixture is constructed, which makes it possible to determine the rheological model of the pumped oil mixture depending on the solvent concentration and the temperature of the mixture. Formulas for forecasting the rheological properties of the oil mixture on the basis of statistical processing of the results of experimental studies are theoretically substantiated. It is proven that the viscosity of binary oil mixtures in the Newtonian fluid field should be determined by a modified Arrhenius equation. The proposed models with a high degree of accuracy describe the rheological properties of the oil mixture. It is shown that in the case of complex mixtures, not one rheological model should be applied, but their hierarchy should be established depending on the solvent concentration and temperature.

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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 (December 27, 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 further modification of them with carbon nanostructures (CNT). The technical result of the given method of mixing CNT and main component of dry construction mixtures - cement – is that it makes possible to use microquantities (0.005%) of CNT in DCM. That allows decreasing product cost of obtained mixture. Moreover, due to increased strength, faster hardening of materials one can reduce consumption of these mixtures. That is additional factor affecting decrease of mixture product cost. The results obtained after application of mechanoactivation of basic mixture components were different practically by all indicators from the mixtures prepared by simple mixing. Compression strength and tensile strength increased by 10–15%, adhesion strength increased too. Along with increasing of strength characteristics such an important indicator of DCM as air permeability has decreased. Reduction of total volume of pores in dense structure of cement matrix caused dramatic slow-up of moisture diffusion rate. The specialists can also be interested in the following inventions in the area of nanotechnologies: a method of laser building-up welding for metal coatings, high RAP in WMA surface mixture containing nanoglass fibers, a device to apply nanoparticles of metal oxides on metal surface under normal conditions, multifunctional nanostructured additive for coatings, experimental assessment of cement mortar using nanooxide compounds, a composition for setting constructional layers of road pavements, a method to obtain composite films of nanofibers, nano-engineering of construction materials using molecular dynamics simulations, cast and self-compacting concrete mixture for cast-in-situ concrete and prefabricated reinforced units, a method to obtain photocatalyst based on nanotubular titanium dioxide et al

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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 intrinsic physical properties of astrophysical sources and extrinsic systematics. Furthermore, we aim to have meaningful estimates of the full photo-z probability distribution, and their uncertainties. Methods. We performed a probabilistic photo-z determination using mixture density networks (MDN). The training data set is composed of optical (griz photometric bands) point-spread-function and model magnitudes and extinction measurements from the SDSS-DR15 and WISE mid-infrared (3.4 μm and 4.6 μm) model magnitudes. We used infinite Gaussian mixture models to classify the objects in our data set as stars, galaxies, or quasars, and to determine the number of MDN components to achieve optimal performance. Results. The fraction of objects that are correctly split into the main classes of stars, galaxies, and quasars is 94%. Furthermore, our method improves the bias of photometric redshift estimation (i.e., the mean Δz = (zp − zs)/(1 + zs)) by one order of magnitude compared to the SDSS photo-z, and it decreases the fraction of 3σ outliers (i.e., 3 × rms(Δz) < Δz). The relative, root-mean-square systematic uncertainty in our resulting photo-zs is down to 1.7% for benchmark samples of low-redshift galaxies (zs < 0.5). Conclusions. We have demonstrated the feasibility of machine-learning-based methods that produce full probability distributions for photo-z estimates with a performance that is competitive with state-of-the art techniques. Our method can be applied to wide-field surveys where extinction can vary significantly across the sky and with sparse spectroscopic calibration samples. The code is publicly available.

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Jalali, Assad, and John Pemberton. "Mixture models for time series." Journal of Applied Probability 32, no. 1 (March 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 autocovariance function. This is shown to be of ARMA type. Finally we give two examples, the second of which extends from the finite to the countable mixture case. The general theory for this extension will be given elsewhere.

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

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Brooks, S. P., B. J. T. Morgan, M. S. Ridout, and S. E. Pack. "Finite Mixture Models for Proportions." Biometrics 53, no. 3 (September 1997): 1097. http://dx.doi.org/10.2307/2533567.

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Journal articles on the topic 'Mixture models'

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