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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.

This paper aims to estimate the determinants of the intensity of use of financial inclusion by households in Ghana.

Due to the reality of a household using one or more financial products or services, this study uses the generalised Poisson model applied to GLSS6 and GLSS7 data collected in 2012/2013 and 2016/2017 respectively, to estimate the determinants of the intensity of use of financial inclusion. To deepen the analysis, a multinomial probit model is also applied.

Results show that infrastructural variables such as roads, public transport and banks stimulate the intensity of financial inclusion. In addition, agricultural development characteristics such as markets and cooperatives are essential for the intensity of inclusion.

There is a need to incorporate how many services or depth of services that people use as part of the conceptualisation of financial inclusion, as this can provide more policy-relevant evidence to enhance priority setting in financial inclusion policies. Also, micro-level financial inclusion studies in agrarian economies should consider exploring agricultural development and infrastructure variables in the modelling framework. As lead to further studies, count models of financial inclusion should consider exploring cross-country analysis, the use of panel data, or other methodological approaches to provide more robust evidence.

Previous studies have not modelled financial inclusion based on a count model as a means of measuring intensity though conceptualisations highlight the fact that people use varied financial products or services. Following from this angle, to the best of the authors’ knowledge, this study provides the first attempt at analysing the underlying determinants of the number of financial products or services used by households.

The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue.

The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots.

The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans.

The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

This study focuses on forecasting the price of the most important export crops of vegetables and fruits in Egypt from 2016 to 2030.

The study applied generalized autoregressive conditional heteroskedasticity (GARCH) model and autoregressive integrated moving average (ARIMA) model.

The results show that ARIMA (1,1,1), ARIMA (2.1,2), ARIMA (1,1,0), ARIMA (1,1,2), ARIMA (0,1,0) and ARIMA (1,1,1) are the most appropriate fitted models to evaluate the volatility of price of green beans, tomatoes, onions, oranges, grapes and strawberries, respectively. The results also revealed the presence of ARCH effect only in the case of Potatoes, hence it is suggested that the GARCH approach be used instead. The GARCH (1,1) is found to be a better model in forecasting price of potatoes.

The study of food price volatility in developing countries is essential, since a significant share of household budgets is spent on food in these economies, so forecasting agricultural prices is a substantial requirement for drawing up many economic plans in the fields of agricultural production, consumption, marketing and trade.

Chinese beer consumption has undergone major changes within the last decade. The combination of a growing middle class and greater exposure to foreign products has resulted in a significant increase in beer imports. The authors examined transformations in this market and how beer preferences have changed over time. This study focuses on changes is origin-specific preferences (e.g. German beer and Mexican beer) as reflected by habit formation (i.e. dynamic consumption patterns) and changes in demand sensitivity to expenditure and prices.

The authors estimated Chinese beer demand – differentiated by source – using a generalized dynamic demand model that accounted for habit formation and trends, as well as the immediate and long-run effects of expenditures and prices on demand. The authors employed a rolling regression procedure that allowed for model estimates to vary with time. Preference changes were inferred from the changing demand estimates, with a particular focus on changes in habit formation, expenditure allocating behaviour, and own-price responsiveness.

Results suggest that Chinese beer preferences have changed significantly over the last decade, increasing for Mexican beer, Dutch beer and Belgian beer. German beer once dominated the Chinese market. However, all indicators suggest that German beer preferences are declining.

Although China is the world's third largest beer importing country behind the United States and France. Few studies have focused on this market. While dynamic analyses of alcoholic beverage demand are not new, this is the first study to examine the dynamics of imported beer preferences in China and implications for exporting countries.

The article aims to investigate the effects of ownership and capital structure on postacquisition operating performance.

The article extends the ongoing literature from an operating loss perspective and provides empirical evidence on the probability of acquirers’ operating loss in relation to ownership and capital structure. The operating performance of publicly listed manufacturing firms in China was tracked up to five years since the completion of the mergers and acquisitions (M&A) during 2003–2014.

The empirical results show that, in a five-year postacquisition period, state-owned enterprises (SOEs) are more likely to experience operating loss than non-SOEs. The likelihood of the operating loss is negatively associated with ownership concentration, implying that concentrated ownership may serve as an effective corporate governance mechanism in the emerging economy and improve postacquisition performance. The rise in leverage increases the likelihood of postacquisition operating loss, indicating that the costs of debt may outweigh the benefits.

The findings contribute to the literature on ownership, debt governance and post-M&A performance from an emerging economy perspective.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway.

A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption.

The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe.

The research results are very useful for mitigating the consequences of high-speed rail accidents.

The present study aims to assess the impact of narcissism, self-confidence and auditor's characteristics on audit report readability for companies listed on the Tehran Stock Exchange.

The study’s statistical population comprises firms listed on the Tehran Stock Exchange. The present research used a systematic elimination method, and 1,162 firm-year observations were obtained for seven years from 2012 to 2018. Three variables including auditor tenure, audit fee and audit specialization are used for measuring auditing features. The Fog index is used as a proxy for measuring audit report readability. In addition, in this paper, four regressions, including fixed effects, random effects, pooled and T+1, are used to estimate reliable coefficients.

The findings show a negative and significant relationship between auditor’s characteristics (tenure, fee and specialization) and audit report readability. Moreover, the variables of the auditor’s narcissism, self-confidence and mandatory auditor change have a positive and significant association with audit report readability. This study lends support to the theories of personality disorder and behavioral decision.

Since narcissism and self-confidence are two characteristics that shape an individual’s character and personality, some involved behavioral factors in auditors’ characteristics contribute to their decisions. The effects of these should be detected to enhance the decision-making process. The said factors significantly impact audit report readability. Hence, this paper attempts to assess the effect of the said factors on audit report readability.