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1 – 10 of 738
Open Access
Article
Publication date: 10 May 2021

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.

Details

Smart and Resilient Transportation, vol. 3 no. 2
Type: Research Article
ISSN: 2632-0487

Keywords

Open Access
Article
Publication date: 6 June 2022

Katsuhiro Sugita

The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.

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Abstract

Purpose

The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.

Design/methodology/approach

The paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration.

Findings

In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data.

Originality/value

The paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.

Details

Asian Journal of Economics and Banking, vol. 6 no. 2
Type: Research Article
ISSN: 2615-9821

Keywords

Open Access
Article
Publication date: 16 June 2022

Dejan Živkov and Jasmina Đurašković

This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).

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Abstract

Purpose

This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).

Design/methodology/approach

In the research process, the authors use the Bayesian method of inference for the two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) model and quantile regression.

Findings

The results clearly indicate that oil price uncertainty has a low effect on output in moderate market conditions in the selected countries. On the other hand, in the phases of contraction and expansion, which are portrayed by the tail quantiles, the authors find negative and positive Bayesian quantile parameters, which are relatively high in magnitude. This implies that in periods of deep economic crises, an increase in the oil price uncertainty reduces output, amplifying in this way recession pressures in the economy. Contrary, when the economy is in expansion, oil price uncertainty has no influence on the output. The probable reason lies in the fact that the negative effect of oil volatility is not strong enough in the expansion phase to overpower all other positive developments which characterize a growing economy. Also, evidence suggests that increased oil uncertainty has a more negative effect on industrial production than on real GDP, whereas industrial share in GDP plays an important role in how strong some CEECs are impacted by oil uncertainty.

Originality/value

This paper is the first one that investigates the spillover effect from oil uncertainty to output in CEEC.

Details

Applied Economic Analysis, vol. 31 no. 91
Type: Research Article
ISSN: 2632-7627

Keywords

Open Access
Article
Publication date: 17 October 2019

Mahmoud 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…

3112

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.

Details

Journal of Humanities and Applied Social Sciences, vol. 2 no. 1
Type: Research Article
ISSN: 2632-279X

Keywords

Open Access
Article
Publication date: 25 June 2020

Paula Cruz-García, Anabel Forte and Jesús Peiró-Palomino

There is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by…

1940

Abstract

Purpose

There is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by seminal theoretical models and subsequent expansions. Others are ad-hoc selections. Up to now, there are no studies assessing these models from a Bayesian model uncertainty perspective. This paper aims to analyze this issue for the EU-15 countries for the period 2008-2014, which mainly corresponds to the Great Recession years.

Design/methodology/approach

It follows a Bayesian variable selection approach to analyze, in a first step, which variables of those suggested by the literature are actually good predictors of banks’ net interest margin. In a second step, using a model selection approach, the authors select the model with the best fit. Finally, the paper provides inference and quantifies the economic impact of the variables selected as good candidates.

Findings

The results widely support the validity of the determinants proposed by the seminal models, with only minor discrepancies, reinforcing their capacity to explain net interest margin disparities also during the recent period of restructuring of the banking industry.

Originality/value

The paper is, to the best of the knowledge, the first one following a Bayesian variable selection approach in this field of the literature.

Details

Applied Economic Analysis, vol. 28 no. 83
Type: Research Article
ISSN: 2632-7627

Keywords

Content available
Book part
Publication date: 18 April 2018

Abstract

Details

Safe Mobility: Challenges, Methodology and Solutions
Type: Book
ISBN: 978-1-78635-223-1

Open Access
Article
Publication date: 7 September 2015

Hubert Zangl and Stephan Mühlbacher-Karrer

The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object…

1044

Abstract

Purpose

The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications.

Design/methodology/approach

The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments.

Findings

The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance.

Originality/value

This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 34 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

Content available
Book part
Publication date: 14 November 2022

Karen McGregor Richmond

Abstract

Details

Marketisation and Forensic Science Provision in England and Wales
Type: Book
ISBN: 978-1-83909-124-7

Open Access
Article
Publication date: 7 April 2022

Raquel Delgado-Aguilera Jurado, Victor Fernando Gómez Comendador, María Zamarreño Suárez, Francisco Pérez Moreno, Christian Eduardo Verdonk Gallego and Rosa María Arnaldo Valdes

The purpose of this study is to establish a systematic framework to characterise the safety of air routes, in terms of separation minima infringements (SMIs) between en-route…

Abstract

Purpose

The purpose of this study is to establish a systematic framework to characterise the safety of air routes, in terms of separation minima infringements (SMIs) between en-route aircraft, based on the definition of models known as safety performance functions.

Design/methodology/approach

Techniques with high predictive capability were selected that enable both expert knowledge and data to be harnessed: Bayesian networks. It was necessary to establish a conceptual framework that integrates the knowledge currently available on the causality and precursors of SMIs with the hindsight derived from the analysis of the type of data available. To translate the conceptual framework into a set of causal subnets, the concepts of air traffic management (ATM) barrier model and event trees have been incorporated.

Findings

The model combines analytics and insights, as well as predictive capability, to answer the question of how airspace separation infringements are produced and what their frequency of occurrence will be. The main outputs of the network are the predicted probability of success for the ATM barriers and the predicted probability distribution of the vertical and horizontal separation of an aircraft in its closest point of approach.

Originality/value

The main contribution of this work is that, by virtue of the calculation capacity obtained, the network can be used to draw conclusions about the impact that a modification of the airspace and of the traffic, or operational conditions, would have on the effectiveness of the barriers and on the final distributions of distance between aircraft in the CPA, thereby estimating the probability of SMI.

Details

Aircraft Engineering and Aerospace Technology, vol. 94 no. 9
Type: Research Article
ISSN: 1748-8842

Keywords

Open Access
Article
Publication date: 12 June 2019

Ximena Alejandra Flechas Chaparro, Leonardo Augusto de Vasconcelos Gomes and Paulo Tromboni de Souza Nascimento

The purpose of this paper is to identify how project portfolio selection (PPS) methods have evolved and which approaches are more suitable for radical innovation projects. This…

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Abstract

Purpose

The purpose of this paper is to identify how project portfolio selection (PPS) methods have evolved and which approaches are more suitable for radical innovation projects. This paper addressed the following research question: how have the selection approaches evolved to better fit within radical innovation conditions? The current literature offers a number of selection approaches with different and, in some cases, conflicting nature. Therefore, there is a lack of understanding regarding when and how to use these approaches in order to select a specific type of innovation projects (from incremental to more radical ones).

Design/methodology/approach

Given the nature of the research question, the authors perform a systematic literature review method and analyze 48 portfolio selection approaches. The authors then classified and characterized these articles in order to identify techniques, tools, required data and types of examined projects, among other aspects.

Findings

The authors identify four key features related to the selection of radical innovation projects: dynamism, interdependency management, uncertainty treatment and required input data. Based on the content analysis, the authors identified that approaches based on different sources and nature of data are more appropriated for uncertain conditions, such as behavioral methods, information gap theory, real options and integrated approaches.

Originality/value

The research provides a comprehensive framework about PPS methods and how they have been evolving over time. This portfolio selection framework considers the particular aspects of incremental and radical innovation projects. The authors hope that the framework contributes to reinvigorating the literature on selection approaches for innovation projects.

Details

Revista de Gestão, vol. 26 no. 3
Type: Research Article
ISSN: 2177-8736

Keywords

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