Letteratura scientifica selezionata sul tema "Finite mixtures"

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Articoli di riviste sul tema "Finite mixtures"

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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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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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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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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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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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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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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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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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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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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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Tesi sul tema "Finite mixtures"

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Baeshu, Abdurrazagh M. "Finite mixtures of generalized Pareto distributions with applications." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244856.

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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Identifying mixtures of mixtures using Bayesian estimation." Taylor & Francis, 2017. http://dx.doi.org/10.1080/10618600.2016.1200472.

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The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this
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Sial, Sultan. "A finite volume method for binary and surfactant mixtures." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21314.pdf.

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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number
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Das, Prabir Kumar. "Ageing of Asphalt Mixtures : Micro-scale and mixture morphology investigation." Doctoral thesis, KTH, Väg- och banteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-145051.

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There are many variables that affect the viscoelastic properties of asphalt mixtures with time, among which age hardening may be considered one of the important ones. Age hardening of asphalt mixtures is an irreversible process, which contributes to a reduction of the durability of pavements and eventually increases the maintenance cost. Beside the environmental effects, ageing in asphalt mixture depends on the physicochemical properties of bitumen and mixture morphology which is a combined effect of aggregate packing, porosity, air void distribution and their interconnectivity. Thus, a clear
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Marek, Petr. "Gaussian mixtures in R." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193077.

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Using Gaussian mixtures is a popular and very flexible approach to statistical modelling. The standard approach of maximum likelihood estimation cannot be used for some of these models. The estimates are, however, obtainable by iterative solutions, such as the EM (Expectation-Maximization) algorithm. The aim of this thesis is to present Gaussian mixture models and their implementation in R. The non-trivial case of having to use the EM algorithm is assumed. Existing methods and packages are presented, investigated and compared. Some of them are extended by custom R code. Several exhaustive simu
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Huber, Florian. "Dealing with heterogeneity in panel VARs using sparse finite mixtures." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6247/1/wp262.pdf.

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In this paper, we provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Fi
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Pringle, Matthew. "Fnite [sic] element modelling of two-component, solid-liquid mixtures /." *McMaster only, 2001.

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Pötzelberger, Klaus. "On the Approximation of finite Markov-exchangeable processes by mixtures of Markov Processes." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1991. http://epub.wu.ac.at/526/1/document.pdf.

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We give an upper bound for the norm distance of (0,1) -valued Markov-exchangeable random variables to mixtures of distributions of Markov processes. A Markov-exchangeable random variable has a distribution that depends only on the starting value and the number of transitions 0-0, 0-1, 1-0 and 1-1. We show that if, for increasing length of variables, the norm distance to mixtures of Markov processes goes to 0, the rate of this convergence may be arbitrarily slow. (author's abstract)<br>Series: Forschungsberichte / Institut für Statistik
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Kendon, Vivien Mary. "Finite Reynolds number effects in fluid mixtures : an investigation using numerical simulation methods." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/12221.

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The scaling theory of the spinodal decomposition of a symmetric binary fluid mixture in the inertial region has been reviewed, and extended by considering energy balance, as well as the momentum balance described by the Navier-Stokes equation (NSE). The prediction for the asymptotic growth rate of the size of the separating fluid domains is ~<I>t<SUP>2/3</SUP></I>, as in simple scaling theory, but the ratio of the nonlinear to viscous terms in the NSE (the Reynolds number) is predicted to remain finite. This is due to the viscous term remaining important to the system dynamics, in contrast to
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Libri sul tema "Finite mixtures"

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Woodruff, David J. Estimation of item response models using the EM algorithm for finite mixtures. American College Testing Program, 1996.

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Woodruff, David. Estimation of item response models using the EM algorithm for finite mixtures. American College Testing Program, 1996.

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Schaffers, Ir. Paulus, J. J. Wave propagation in electrically conducting mixtures of inhomogeneities in liquids. Shaker, 1993.

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McLachlan, Geoffrey, and David Peel. Finite Mixture Models. John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471721182.

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Paetz, Friederike. Finite Mixture Multinomiales Probitmodell. Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-02662-2.

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Lachos Dávila, Víctor Hugo, Celso Rômulo Barbosa Cabral, and Camila Borelli Zeller. Finite Mixture of Skewed Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98029-4.

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service), SpringerLink (Online, ed. Medical Applications of Finite Mixture Models. Springer-Verlag Berlin Heidelberg, 2009.

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Titterington, D. M. Statistical analysis of finite mixture distributions. Wiley, 1985.

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Finite Mixture Distributions. Springer, 2011.

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Peel, David, and Geoffrey J. McLachlan. Finite Mixture Models. Wiley & Sons, Incorporated, John, 2007.

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Capitoli di libri sul tema "Finite mixtures"

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Aitkin, Murray. "How many Components in a Finite Mixture?" In Mixtures. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119995678.ch13.

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Li, Feng, Mattias Villani, and Robert Kohn. "Modelling Conditional Densities Using Finite Smooth Mixtures." In Mixtures. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119995678.ch6.

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Green, Peter J. "Introduction to Finite Mixtures." In Handbook of Mixture Analysis. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429055911-1.

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Chen, Jiahua, and Pengfei Li. "The Limiting Distribution of the EM Test of the Order of a Finite Mixture." In Mixtures. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119995678.ch3.

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Archambeau, Cédric, and Michel Verleysen. "Manifold Constrained Finite Gaussian Mixtures." In Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_100.

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Celeux, Gilles. "EM Methods for Finite Mixtures." In Handbook of Mixture Analysis. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429055911-2.

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Lachos Dávila, Víctor Hugo, Celso Rômulo Barbosa Cabral, and Camila Borelli Zeller. "Maximum Likelihood Estimation in Normal Mixtures." In Finite Mixture of Skewed Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98029-4_2.

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Lachos Dávila, Víctor Hugo, Celso Rômulo Barbosa Cabral, and Camila Borelli Zeller. "Scale Mixtures of Skew-Normal Distributions." In Finite Mixture of Skewed Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98029-4_3.

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Kontkanen, Petri, Petri Myllymäki, Tomi Silander, and Henry Tirri. "Batch classifications with discrete finite mixtures." In Machine Learning: ECML-98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026691.

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Grün, Bettina, and Friedrich Leisch. "Finite Mixtures of Generalized Linear Regression Models." In Recent Advances in Linear Models and Related Areas. Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2064-5_11.

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Atti di convegni sul tema "Finite mixtures"

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Morelande, Mark R., and Branko Ristic. "Bayesian analysis of finite Gaussian mixtures." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495791.

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Chen, Jiahua. "Distributed Learning of Finite Gaussian Mixtures." In 3nd International Conference on Statistics: Theory and Applications (ICSTA'21). Avestia Publishing, 2021. http://dx.doi.org/10.11159/icsta21.001.

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Ligo, Jonathan G., George V. Moustakides, and Venugopal V. Veeravalli. "Detection of sparse mixtures: The finite alphabet case." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869572.

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Zhang, Qiong, and Jiahua Chen. "Robustness of Gaussian Mixture Reduction for Split-and-Conquer Learning of Finite Gaussian Mixtures." In 3nd International Conference on Statistics: Theory and Applications (ICSTA'21). Avestia Publishing, 2021. http://dx.doi.org/10.11159/icsta21.135.

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Tsvetkov, Dimiter, and Lyubomir Hristov. "Parameter estimation for finite mixtures of partial credit models." In the 2007 international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1330598.1330664.

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Farcas, Cristian, Rodica Cret, Dorin Petreus, and Niculaie Palaghrta. "Modeling and simulation of dielectric mixtures using finite elements method." In 2010 IEEE 16th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2010. http://dx.doi.org/10.1109/siitme.2010.5650814.

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Jinwen Ma. "Automated Model Selection (AMS) on Finite Mixtures: A Theoretical Analysis." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246961.

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Tsvetkov, Dimiter, and Lyubomir Hristov. "Parameter estimation for finite mixtures of generalized partial credit models." In the 9th International Conference. ACM Press, 2008. http://dx.doi.org/10.1145/1500879.1500910.

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Nagy, Ivan, Evgenia Suzdaleva, and Tereza Mlynarova. "Comparison of state estimation using finite mixtures and hidden Markov models." In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2011. http://dx.doi.org/10.1109/idaacs.2011.6072822.

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Stibor, Thomas. "Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions." In the 10th annual conference. ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389113.

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Rapporti di organizzazioni sul tema "Finite mixtures"

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Rahman, Mohammad, Ahmed Ibrahim, and Riyadh Hindi. Bridge Decks: Mitigation of Cracking and Increased Durability—Phase III. Illinois Center for Transportation, 2020. http://dx.doi.org/10.36501/0197-9191/20-022.

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Early-age cracking in concrete decks significantly reduces the service life of bridges. This report discusses the application of various concrete mixtures that include potential early mitigation ingredients. Large-scale (7 ft × 10 ft) experimental bridge prototypes with similar restraint conditions found in actual bridges were poured with different concrete mixtures to investigate mitigation techniques. Portland cement (control), expansive Type K cement, internally cured lightweight aggregate (LWA), shrinkage-reducing admixture (SRA), and gypsum mineral were investigated as mitigating ingredie
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Chen, Xiaohong, Elie Tamer, and Maria Ponomareva. Likelihood inference in some finite mixture models. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.1913.

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Steele, Russell J., Adrian E. Raftery, and Mary J. Emond. Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS). Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459853.

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Linton, Oliver, and Heather Battey. Nonparametric estimation of multivariate elliptic densities via finite mixture sieves. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.1513.

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Battey, Heather, and Oliver Linton. Nonparametric estimation of multivariate elliptic densities via finite mixture sieves. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.4113.

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Solka, Jeffrey L., Wendy L. Poston, and Edward J. Wegman. A New Visualization Technique to Study the Time Evolution of Finite and Adaptive Mixture Estimators. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada291147.

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Xiao, Ruli, and Yingyao Hu. Global estimation of finite mixture and misclassi fication models with an application to multiple equilibria. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.3218.

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Lee, Chih-Jen, Thomas White, and Terry West. Effect of Fine Aggregate Angularity on Asphalt Mixture Performance. Purdue University, 2000. http://dx.doi.org/10.5703/1288284313227.

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Lee, Youngrok. Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1116720.

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Roesler, Jeffery, Roberto Montemayor, John DeSantis, and Prakhar Gupta. Evaluation of Premature Cracking in Urban Concrete Pavement. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-001.

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This study investigated the causes for premature, transverse cracking on urban jointed plain concrete pavements in Illinois. A field survey of 67 sections throughout Illinois coupled with ultrasonic evaluation was completed to synthesize the extent of premature cracking on urban JPCP. The visual survey showed some transverse and longitudinal cracks were a result of improper slab geometry (excessive slab length and width). Ultrasonic tests over the contraction joints determined some notched joints had not activated and adjacent transverse cracks were likely formed as a result. Three-dimensional
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