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1 – 10 of 286Mahmoud ELsayed and Amr Soliman
The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the…
Abstract
Purpose
The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method.
Design/methodology/approach
In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques.
Findings
These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.
Originality/value
In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.
Daniel Watzenig, Markus Neumayer and Colin Fox
The purpose of this paper is to establish a cheap but accurate approximation of the forward map in electrical capacitance tomography in order to approach robust real‐time…
Abstract
Purpose
The purpose of this paper is to establish a cheap but accurate approximation of the forward map in electrical capacitance tomography in order to approach robust real‐time inversion in the framework of Bayesian statistics based on Markov chain Monte Carlo (MCMC) sampling.
Design/methodology/approach
Existing formulations and methods to reduce the order of the forward model with focus on electrical tomography are reviewed and compared. In this work, the problem of fast and robust estimation of shape and position of non‐conducting inclusions in an otherwise uniform background is considered. The boundary of the inclusion is represented implicitly using an appropriate interpolation strategy based on radial basis functions. The inverse problem is formulated as Bayesian inference, with MCMC sampling used to efficiently explore the posterior distribution. An affine approximation to the forward map built over the state space is introduced to significantly reduce the reconstruction time, while maintaining spatial accuracy. It is shown that the proposed approximation is unbiased and the variance of the introduced additional model error is even smaller than the measurement error of the tomography instrumentation. Numerical examples are presented, avoiding all inverse crimes.
Findings
Provides a consistent formulation of the affine approximation with application to imaging of binary mixtures in electrical tomography using MCMC sampling with Metropolis‐Hastings‐Green dynamics.
Practical implications
The proposed cheap approximation indicates that accurate real‐time inversion of capacitance data using statistical inversion is possible.
Originality/value
The proposed approach demonstrates that a tolerably small increase in posterior uncertainty of relevant parameters, e.g. inclusion area and contour shape, is traded for a huge reduction in computing time without introducing bias in estimates. Furthermore, the proposed framework – approximated forward map combined with statistical inversion – can be applied to all kinds of soft‐field tomography problems.
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Firano Zakaria and Anass Benbachir
One of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the…
Abstract
Purpose
One of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the stochastic volatility of the MAD/EURO and MAD/USD exchange rates.
Design/methodology/approach
For this purpose, the authors have adopted Bayesian approach based on the MCMC (Monte Carlo Markov Chain) algorithm which permits to reproduce the main stylized empirical facts of the assets studied. The data used in this study are the daily historical series of MAD/EURO and MAD/USD exchange rates covering the period from February 2, 2000, to March 3, 2017, which represent 4,456 observations.
Findings
By the aid of this approach, the authors were able to estimate all the random parameters of the stochastic volatility model which permit the prediction of the future exchange rates. The authors also have simulated the histograms, the posterior densities as well as the cumulative averages of the model parameters. The predictive efficiency of the stochastic volatility model for Morocco is capable to facilitate the management of the exchange rate in more flexible exchange regime to ensure better targeting of monetary and exchange policies.
Originality/value
To the best of the authors’ knowledge, the novelty of the paper lies in the production of a tool for predicting the evolution of the Moroccan exchange rate and also the design of a tool for the monetary authorities who are today in a proactive conception of management of the rate of exchange. Cyclical policies such as monetary policy and exchange rate policy will introduce this type of modelling into the decision-making process to achieve a better stabilization of the macroeconomic and financial framework.
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Markus Neumayer, Thomas Suppan and Thomas Bretterklieber
The application of statistical inversion theory provides a powerful approach for solving estimation problems including the ability for uncertainty quantification (UQ) by means of…
Abstract
Purpose
The application of statistical inversion theory provides a powerful approach for solving estimation problems including the ability for uncertainty quantification (UQ) by means of Markov chain Monte Carlo (MCMC) methods and Monte Carlo integration. This paper aims to analyze the application of a state reduction technique within different MCMC techniques to improve the computational efficiency and the tuning process of these algorithms.
Design/methodology/approach
A reduced state representation is constructed from a general prior distribution. For sampling the Metropolis Hastings (MH) Algorithm and the Gibbs sampler are used. Efficient proposal generation techniques and techniques for conditional sampling are proposed and evaluated for an exemplary inverse problem.
Findings
For the MH-algorithm, high acceptance rates can be obtained with a simple proposal kernel. For the Gibbs sampler, an efficient technique for conditional sampling was found. The state reduction scheme stabilizes the ill-posed inverse problem, allowing a solution without a dedicated prior distribution. The state reduction is suitable to represent general material distributions.
Practical implications
The state reduction scheme and the MCMC techniques can be applied in different imaging problems. The stabilizing nature of the state reduction improves the solution of ill-posed problems. The tuning of the MCMC methods is simplified.
Originality/value
The paper presents a method to improve the solution process of inverse problems within the Bayesian framework. The stabilization of the inverse problem due to the state reduction improves the solution. The approach simplifies the tuning of MCMC methods.
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Mohd Irfan and Anup Kumar Sharma
A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior…
Abstract
Purpose
A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.
Design/methodology/approach
In this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.
Findings
The Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.
Originality/value
The ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.
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The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques.
Abstract
Purpose
The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques.
Design/methodology/approach
Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts.
Findings
The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector.
Originality/value
This is original research.
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Chao Yu, Haiying Li, Xinyue Xu and Qi Sun
During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a…
Abstract
Purpose
During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.
Design/methodology/approach
First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.
Findings
The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.
Originality/value
First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.
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A. George Assaf and Mike G. Tsionas
This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.
Abstract
Purpose
This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.
Design/methodology/approach
The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.
Findings
The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.
Research limitations/implications
There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.
Originality/value
With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.
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Harindranath R.M. and Jayanth Jacob
This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA)…
Abstract
Purpose
This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.
Design/methodology/approach
Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.
Findings
The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.
Practical implications
The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.
Originality/value
This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.
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Mohammed Ayoub Ledhem and Mohammed Mekidiche
This paper aims to investigate empirically whether Islamic securities enhance economic growth in the Southeast Asian region based on the endogenous growth theory using the…
Abstract
Purpose
This paper aims to investigate empirically whether Islamic securities enhance economic growth in the Southeast Asian region based on the endogenous growth theory using the non-parametric analysis.
Design/methodology/approach
This paper applies panel quantile regression with Markov chain Monte Carlo optimization as an optimal non-parametric approach to investigate the effect of Islamic securities on economic growth starting from 2013Q4 to 2019Q4 in Southeast Asia. Total issued Islamic securities holdings are employed as a measure for Islamic securities, while the gross domestic product is employed as a proxy for economic growth. The sample includes all working Islamic financial foundations in the top progressive Islamic securities markets' countries of Southeast Asia (Malaysia, Indonesia and Brunei Darussalam).
Findings
The findings confirm that the increase of issuing Islamic securities in Islamic capital markets of Southeast Asia is increasing the levels of economic growth, reflecting the weighty role of the Islamic capital market development as an active contributor to economic growth.
Practical implications
This research would fill the literature gap by exploring Islamic securities–economic growth nexus in Southeast Asia using a robust non-parametric approach based on the endogenous growth theory for better estimation results. The findings of this review serve as a roadmap for financial analysts, policymakers and decision makers to stimulate the Islamic securities markets as another source of finance which can promote the economic growth.
Originality/value
This research is the first that investigates empirically the Islamic securities–economic growth nexus in Southeast Asia using a new empirical investigation built on the non-parametric analysis and outlined within the theoretical context of the endogenous growth model to gain robust evidence about this nexus.
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