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The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate.

Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring.

The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently.

The PGLM with log link has not been used to monitor multinomial profiles in Phase I.

This paper seeks to examine the profile of artifacts with superior returns in order to identify the usage of management accounting in a Brazilian context.

This paper is part of an empirical research project based on a probabilistic sample (119 entities) from medium and large Brazilian companies, selected according to economic sector and revenues. The management accounting artifacts were identified according to the five stages of International Management Accounting Practice 1 (IMAP 1, International Federation of Accountants (IFA), 1998). Logistic regression was applied to identify the artifacts most adherent to companies with the outstanding profile.

In the analysis of the five stages of IMAP 1, only the fifth stage, value management, provided the significance level to accept the hypothesis. In this stage, the artifacts that were accepted with a significance level of 90 percent were return on equity and balanced scorecard.

The field research was applied only in the Brazilian market.

Especially for researchers, this paper raises some important questions, and aims to stimulate future studies in management accounting.

This paper contributes by presenting research from outside the Anglo‐Saxon world, and by analyzing the artifacts' profile with approaches balanced between positive and qualitative accounting.

The purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.

The proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.

The simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.

The research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.

Due to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

The focus on new technologies in service situations is growing and is of particular importance in financial‐services contexts. It is argued that there is mutuality of benefit for both bank and customer through the adoption of self‐service technologies (SSTs), of which e‐banking is but one example. Having established problems in the use of conventional segmentation methods, this paper reports on a study into Internet banking that focuses on the extent to which 480 retail‐bank customers can be clustered according to an adapted decision‐making framework. How such clusters can help influence the adoption of the Internet‐banking interface is explored. Findings show an encouraging match between the four sample clusters identified from the case bank and the a priori classification of decision styles. This raises opportunities for the case bank's marketing strategy in terms of offering greater insight into the motivations for the adoption of e‐banking solutions within the customer base. High levels of Internet use at work are seen to positively influence e‐banking registration.

Trans women of color contend with multiple marginalizations; the purpose of this study is to examine associations between experiencing discriminatory (racist/transphobic) events and depression symptoms. It uses a categorical measure of combined discrimination, and examines a protective association of transgender identity on depression symptoms.

Data from a subset of trans women of color participants in the Sheroes study were analyzed with linear and logistic regression. Associations of depression symptoms with racist and transphobic events, combined discrimination, coping self-efficacy, and transgender identity were assessed with odds ratios.

Exposure to discriminatory events and combined discrimination positively associated with depression symptom odds. Increased transgender identity associated with increased coping self-efficacy, which negatively associated with depression symptom odds.

Cross-sectional study data prohibits inferring causality; results support conducting longitudinal research on discrimination's health effects, and research on transgender identity. Results also support operationalizing intersectionality in health research. The study's categorical approach to combined discrimination may be replicable in studies with hard to reach populations and small sample sizes.

Health programs could pursue psychosocial interventions and anti-discrimination campaigns. Interventions might advocate increasing participants’ coping self-efficacy while providing space to explore and develop social identity.

There is a need for policy and health programs to center trans women of color concerns.

This study examines combined discrimination and identity in relation to depression symptoms among trans women of color, an underserved population.

The growth in cryptomarkets has reinvigorated the research on illicit drug distribution due to the availability of large-scale data. This data has enabled researchers to ask new and detailed questions about how participants in these markets trust each other enough for the market not to collapse. This question deserves more attention because it has become a taken-for-granted notion that repeated transactions and social categories create trust. Whether online or on the street, economic exchanges under illegality are more uncertain than transactions in the legal economy. This puts higher demands on trust, as there is less information and the stakes are higher. In this chapter, the author presents definitions, typologies, and disciplinary contributions to the study of trust and examine how it has been operationalised in a sample of 13 peer-reviewed articles. These articles focus on three dimensions of trust: process-based trust that derives from repeated transactions with known partners; character-based trust measured by the networked reputation scores; and institutional-based trust in the platform and its administrators. In practice, the trust bases are intertwined. Drawing on the broader social science literature on trust, a mesolevel operationalisation that centres on networked reputation scores as embedded in processes and institutions can draw the research together in a multidisciplinary framework.

This study aims to assess the risk profile of millennial investors residing in the Delhi NCR region. In addition, the relationship between the risk profile and demographic traits of millennial investors was also analyzed.

Data was collected using a structured questionnaire segregated into two sections. In the first section, millennials were asked questions on socio-demographic factors, and the second section contained ten Likert-type statements to cover the multidimensionality of financial risk. Factor analysis and one-way ANOVA were used to analyze the primary data collected for this study.

The findings indicate that the risk profile of millennials is mainly affected by three factors: risk-taking capacity, risk attitude and risk propensity. Except for educational qualification and occupation, all other demographic features, such as age, gender, marital status, income and family size, seem to significantly influence the factors defining millennials' risk profile.

Uncertainty is inherent in any financial decision, and an investor’s willingness to deal with these variations determines their investment risk profile. To make sound financial decisions, it is mandatory to understand one’s risk profile. The awareness of millennials' distinctive risk profile will come in handy to financial stakeholders because they account for one-third of India’s population, and their financial decisions will shape the financial world for the decades to come.

When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.