Search results

1 – 10 of over 15000
To view the access options for this content please click here
Book part
Publication date: 15 January 2010

Isobel 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…

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.

Details

Choice Modelling: The State-of-the-art and The State-of-practice
Type: Book
ISBN: 978-1-84950-773-8

To view the access options for this content please click here
Article
Publication date: 8 May 2018

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.

Details

Engineering Computations, vol. 35 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

To view the access options for this content please click here
Article
Publication date: 26 June 2019

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…

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.

Details

International Journal of Structural Integrity, vol. 10 no. 4
Type: Research Article
ISSN: 1757-9864

Keywords

To view the access options for this content please click here
Book part
Publication date: 30 August 2019

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…

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.

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
ISBN: 978-1-78973-241-2

Keywords

To view the access options for this content please click here
Article
Publication date: 1 July 2014

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…

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.

Details

Agricultural Finance Review, vol. 74 no. 2
Type: Research Article
ISSN: 0002-1466

Keywords

To view the access options for this content please click here
Article
Publication date: 1 February 1995

Chulho Jung

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…

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.

Details

Journal of Economic Studies, vol. 22 no. 1
Type: Research Article
ISSN: 0144-3585

Keywords

To view the access options for this content please click here
Book part
Publication date: 30 September 2014

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…

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.

Details

Economic Well-Being and Inequality: Papers from the Fifth ECINEQ Meeting
Type: Book
ISBN: 978-1-78350-556-2

Keywords

Content available
Article
Publication date: 29 July 2020

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…

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.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2210-8327

Keywords

To view the access options for this content please click here
Article
Publication date: 14 August 2017

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…

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.

Details

Multidiscipline Modeling in Materials and Structures, vol. 13 no. 2
Type: Research Article
ISSN: 1573-6105

Keywords

To view the access options for this content please click here
Book part
Publication date: 24 March 2006

Alejandro Villagran and Gabriel Huerta

The problem of model mixing in time series, for which the interest lies in the estimation of stochastic volatility, is addressed using the approach known as Mixture

Abstract

The problem of model mixing in time series, for which the interest lies in the estimation of stochastic volatility, is addressed using the approach known as Mixture-of-Experts (ME). Specifically, this work proposes a ME model where the experts are defined through ARCH, GARCH and EGARCH structures. Estimates of the predictive distribution of volatilities are obtained using a full Bayesian approach. The methodology is illustrated with an analysis of a section of US dollar/German mark exchange rates and a study of the Mexican stock market index using the Dow Jones Industrial index as a covariate.

Details

Econometric Analysis of Financial and Economic Time Series
Type: Book
ISBN: 978-1-84950-388-4

1 – 10 of over 15000