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1 – 10 of over 18000Isobel Claire Gormley and Thomas Brendan Murphy
Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys…
Abstract
Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.
To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.
Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.
Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.
Farhang Behrangi, Mohammad Ali Banihashemi, Masoud Montazeri Namin and Asghar Bohluly
This paper aims to present a novel numerical technique for solving the incompressible multiphase mixture model.
Abstract
Purpose
This paper aims to present a novel numerical technique for solving the incompressible multiphase mixture model.
Design/methodology/approach
The multiphase mixture model contains a set of momentum and continuity equations for the mixture phase, a second phase continuity equation and the algebraic equation for the relative velocity. For solving continuity equation for the second phase and advection term of momentum, an improved approach fine grid advection-multiphase mixture flow (FGA-MMF) is developed. In the FGA-MMF method, the continuity equation for the second phase is solved with higher-order schemes in a two times finer grid. To solve the advection term of the momentum equation, the advection fluxes of the volume fraction in the continuity equation for the second phase are used.
Findings
This approach has been used in various tests to simulate unsteady flow problems. Comparison between numerical results and experimental data demonstrates a satisfactory performance. Numerical examples show that this approach increases the accuracy and stability of the solution and decreases non-monotonic results.
Research limitations/implications
The solver for the multi-phase mixture model can only be adopted to solve the incompressible fluid flow.
Originality/value
The paper developed an innovative solution (FGA-MMF) to find multi-phase flow field value in the multi-phase mixture model. Advantages of the FGA-MMF technique are the ability to accurately determine the phases interpenetrating, decreasing the numerical diffusion of the interface and preventing instability and non-monotonicity in solution of large density variation problems.
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Wang Lijun and Li Qingbo
Asphalt mixture is widely used in road engineering, and its performance research is particularly important. But the study of asphalt mixture performance needs a lot of tests, such…
Abstract
Purpose
Asphalt mixture is widely used in road engineering, and its performance research is particularly important. But the study of asphalt mixture performance needs a lot of tests, such as bending test, splitting test and so on. It also needs a lot of time and material resources. The purpose of this paper is to obtain test results through finite element numerical simulation, and show that this saves a lot of manpower and material resources.
Design/methodology/approach
The mechanical parameters of the material are obtained through uniaxial compression tests. The true stress and plastic strain are calculated according to nominal stress and nominal strain. A constitutive model is established. Then a finite element model of asphalt mixture is established. The numerical simulation and performance study of asphalt mixture bending test is carried out. At the same time, according to the above method, the asphalt mixture is subjected to freeze-thaw cycles and ultraviolet aging, and the mechanical parameters are obtained by a uniaxial compression test. A numerical model is established to simulate the bending characteristics of asphalt mixture after freeze-thaw cycles and ultraviolet aging.
Findings
A uniaxial compression test of the asphalt mixture is conducted to obtain nominal stress and nominal strain. The true stress and plastic strain are calculated and the elastic modulus is established with Poisson’s ratio as the elastic part, and the true stress and plastic strain as the plastic part. The model is constructed, the finite element model is established and the bending test is numerically simulated. The verified trend is consistent, and the method is feasible. According to the above method, the concrete is subjected to freeze-thaw cycle and ultraviolet aging, and the finite element model is established by using uniaxial compression test to obtain parameters. The bending test is simulated and the verification method is feasible. With the increase of the number of freeze-thaw cycles and the increase of UV aging time, the maximum bending strain of SBS modified asphalt mixture and matrix asphalt mixture is decreased .The low-temperature performance of SBS modified asphalt mixture is better than that of matrix asphalt mixture.
Originality/value
A method of simulating asphalt mixture test by finite element method numerical simulation is established. By using this method, the performance of asphalt mixture is studied, which saves a lot of manpower and material resources. At the same time, this method can be used to study the characteristics of asphalt mixture under complex conditions.
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Timothy Cogley and Richard Startz
Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for…
Abstract
Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.
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Lysa Porth, Wenjun Zhu and Ken Seng Tan
The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a…
Abstract
Purpose
The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a scientific pricing framework.
Design/methodology/approach
The generating process of the historical loss cost ratio's (LCR's) are reviewed, and the Erlang mixture distribution is proposed. A modified credibility approach is developed based on the Erlang mixture distribution and the liability weighted LCR, and information from the observed data of the individual region/province is integrated with the collective experience of the entire crop reinsurance program in Canada.
Findings
A comprehensive data set representing the entire crop insurance sector in Canada is used to show that the Erlang mixture distribution captures the tails of the data more accurately compared to conventional distributions. Further, the heterogeneous credibility premium based on the liability weighted LCR's is more conservative, and provides a more scientific approach to enhance the reinsurance pricing.
Research limitations/implications
Credibility models are in the early stages of application in the area of agriculture insurance, therefore, the credibility models presented in this paper could be verified with data from other geographical regions.
Practical implications
The credibility-based Erlang mixture model proposed in this paper should be useful for crop insurers and reinsurers to enhance their ratemaking frameworks.
Originality/value
This is the first paper to introduce the Erlang mixture model in the context of agricultural risk modeling. Two modified versions of the Bühlmann-Straub credibility model are also presented based on the liability weighted LCR to enhance the reinsurance pricing framework.
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Develops a method of forecasting foreign exchange rate by normalmixture model (NMM). Initially establishes a set of exchange rate modelsand switches from one model to another…
Abstract
Develops a method of forecasting foreign exchange rate by normal mixture model (NMM). Initially establishes a set of exchange rate models and switches from one model to another probabilistically, depending on supply shocks or government policy changes. By assuming that the population distribution of foreign exchange rate is a mixture of normal distributions, these models can then be estimated simultaneously. Uses the estimated parameters of the model to forecast foreign exchange rate, and then four foreign exchange rate models are used to estimate the NMM. The out‐of‐sample forecasting results obtained show that we can decrease the mean squared error (MSE) of forecast error dramatically by using the NMM, compared with the MSE of the best forecast of each separate model.
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Abdoul Aziz Ndoye and Michel Lubrano
We provide a Bayesian inference for a mixture of two Pareto distributions which is then used to approximate the upper tail of a wage distribution. The model is applied to the data…
Abstract
We provide a Bayesian inference for a mixture of two Pareto distributions which is then used to approximate the upper tail of a wage distribution. The model is applied to the data from the CPS Outgoing Rotation Group to analyze the recent structure of top wages in the United States from 1992 through 2009. We find an enormous earnings inequality between the very highest wage earners (the “superstars”), and the other high wage earners. These findings are largely in accordance with the alternative explanations combining the model of superstars and the model of tournaments in hierarchical organization structure. The approach can be used to analyze the recent pay gaps among top executives in large firms so as to exhibit the “superstar” effect.
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Abdullah Alharbi, Wajdi Alhakami, Sami Bourouis, Fatma Najar and Nizar Bouguila
We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is…
Abstract
We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.
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Junfeng Sun, Haitao Zhang, Guangyuan Wu, Zuoqiang Liu, Yuping Feng and Minghao Jia
In order to give full play to the function of noise reduction of asphalt pavement, it is necessary to understand its internal sound absorption mechanism. Therefore, the purpose of…
Abstract
Purpose
In order to give full play to the function of noise reduction of asphalt pavement, it is necessary to understand its internal sound absorption mechanism. Therefore, the purpose of this study is to establish a micro model of the pore structure of asphalt mixture with the help of finite element method (FEM), discuss the noise reduction mechanism of asphalt pavement from the micro perspective and analyze and evaluate the noise attenuation law of the pore structure.
Design/methodology/approach
The FEM was used to establish the microscopic model of the pore structure of asphalt mixture. Based on the principle of acoustics, the noise reduction characteristics of asphalt pavement were simulated. The influence of gradation and pore characteristics on the noise reduction performance of asphalt pavement was analyzed.
Findings
The results show that the open graded friction course-13 (OGFC-13) has excellent performance in noise reduction. The resonant sound absorption structure composed of its large porosity can effectively reduce the pavement noise. For asphalt concrete-13 (AC-13) and stone matrix asphalt-13 (SMA-13), the less resonant sound absorption structure makes them have poor sound absorption effect. In addition, the variation rules of noise transmission loss (TL) curve and sound absorption coefficient curve of three graded asphalt mixtures were obtained. At the same time, the peak noise reduction values of OGFC-13, AC-13 and SMA-13 were obtained, which were 650Hz, 1000Hz and 800Hz, respectively.
Originality/value
The results show that the simulation results can well reflect and express the experimental results. This will provide a reference for further exploring the sound absorption mechanism and its variation rule of porous asphalt pavement. It also has some positive significance for the application of low noise asphalt pavement.
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Mohamed Turki, Ines Zarrad, Michéle Quéneudec and Jamel Bouaziz
The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from…
Abstract
Purpose
The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from shredded worn tires and filler using adaptive neuro fuzzy inference systems (ANFIS).
Design/methodology/approach
The volume substitution contains a ratio of rubber aggregates vs sand in mortar and with crushed sand in RCC and ranges from 0 to 50 per cent. As for the filler, they are substituted with sand by 5 per cent in mortar mixture. The methodology consists of optimizing the percentage of substitution in cementitious mixtures to ensure better mechanical properties of materials like compressive strength. The prediction of compressive strength and the optimization of cementitious mixtures encourage their uses in such construction pavements, in area games or in other special constructions. These cementitious materials are considered as friendly to the environment by focussing on their improved deformability.
Findings
The results of this paper show that the performance of the constructed fuzzy method was measured by correlation of experimental and model results of mortar and RCC mixtures containing both rubber aggregates and filler. The comparison between elaborated models through the error and the accuracy calculations confirms the reliability of the ANFIS method.
Originality/value
The purpose of this paper is to assess the performance of the constructed fuzzy model by the ANFIS method for two types of cementitious materials like mortar and RCC containing rubber aggregates and filler. The fuzzy method could predict the compressive strength based on the limited measurement values in the mechanical experiment. Furthermore, the comparison between the elaborated models confirms the reliability of the ANFIS method through the error and the accuracy calculations for the best cementitious material mixtures.
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