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This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.

This study aims to identify the factors that lead non-financial companies listed in the UK to use an internal audit function (IAF) as a monitoring mechanism. Although the use of an IAF in the UK is voluntary, no prior research has examined the drivers for using one.

Financial and non-financial data were collected from the annual reports of 332 UK non-financial companies listed on the London Stock Exchange (LSE) Main Market. Univariate tests and multivariate logistic regression tests were used to test the research hypotheses. A theoretical framework based on both agency theory and transaction cost economics (TCE) theory was used to explain the economic factors affecting the use of an IAF.

The study provides evidence that firm size, level of internal risks, agency problem between owners and managers and existence of an effective audit committee are associated with the existence of an IAF. Thus, the need to have strong internal control and risk management systems and to reduce both internal and external agency costs drives companies to have an IAF. These results suggest the importance of IAF as an internal corporate governance tool and the effectiveness of UK governance regulations in monitoring the effectiveness of internal control systems.

Given the importance of the IAF’s corporate governance role, the study provides some policy implications. Regulators should pay more attention to the issue of maintaining an IAF, especially by large companies, the relationship between the IAF and other governance parties, especially the audit committee, and the disclosure of more relevant information about the IAF’s characteristics and practices.

This is the first study to examine the factors affecting the existence of the IAF within the UK’s distinctive regulatory approach of “comply or disclose reasons”. Furthermore, it provides a theoretical framework that explains how both the agency theory and TCE theory can interpret the adoption of internal audit.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

The purpose of this paper is to investigate the prevalence and factors related to cognitive impairment among hill-tribe older people in Chiang Rai province, Thailand.

A cross-sectional study was carried out amongst 459 hill-tribe older people aged 60 years and above. A Mini Mental State Examination (MMSE) Thai 2002 version was used for cognitive screening. A questionnaire and medical records were used for demographic and clinical data collection while descriptive statistics were used to analyze characteristic data. Potential factors related to cognitive impairment were analyzed by using univariate logistic regression analysis.

The prevalence of cognitive impairment amongst the participants was 49.89 percent (95% CI 45.32%, 53.47 percent). Factors related to cognitive decline included no occupation (OR=1.49, 95% CI 1.10–2.03, p<0.04) and a history of amphetamine use (OR=1.57, 95% CI 1.09–2.33, p<0.04).

Cognitive decline should be a cause for concern amongst Thai hill-tribe older people, especially amongst those in the group with a history of amphetamine use. However, Thai health care professionals need to be aware of the potential cultural bias in the MMSE Thai 2002 version as a cognition test targeted at the hill-tribe population as the questionnaire may not provide a true reflection of their cultural experience and background.

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

Investors label high (low) book-to-market (B/M) firms as value (growth) companies. The conventional wisdom supports that growth stocks grow faster than the value ones, creating greater shareholder value. The Purpose of this paper is to analyze how stocks of growth and value companies create value for their shareholders in Brazil, compared to the USA market. For this, the authors analyze three dimensions of return.

First, the authors perform portfolios to analyze the growth rates of shareholders’ return. Then, the authors perform regressions to study the explanatory power of the B/M in growth. The data come from Thomson Reuters Eikon database and the Brazilian Institute of Geography and Statistics. The authors select all non-financial firms with available data from 1997 to 2017.

The profitability of growth firms is higher than the value ones, in almost every year after the portfolios’ formation, with little variation. Contrary to the findings for the US market, growth companies in Brazil show higher dividend growth than value companies.

It is possible that the database does not contain complete and entirely reliable accounting data, which may partially affect the results.

The findings contradict those exposed in the USA. The implications are the inverse of the US study: the duration-based explanation could be a vital factor for the value premium in the Brazilian stock market. Also, the findings support the standard valuation techniques and help the growth rates estimation in the valuation process (top-down approach).

This study is the first to compare the profitability and dividend growth of growth/value stocks in the Brazilian market. Overall, growth stocks have considerable profitability, and dividend growth compared to value stocks.