A clustering approach to detect multiple outliers in linear functional relationship model for circular data

Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model for circular variables. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering approach. With the use of a tree diagram, we illustrate the detection of outliers graphically. A Monte Carlo simulation study is done to verify the accuracy of the proposed method. Low probability of masking and swamping effects indicate the validity of the proposed approach. Also, the illustrations to two sets of real data are given to show its practical applicability.     

Market structure,bank conduct and bank performance: Evidence from ASEAN

Whether banks in a concentrated market increase their profits through monopoly pricing is a question of prime concern for antitrust policies. We explore this question by introducing the role of bank conduct into the structure–performance relationship. We apply Two-step System GMM dynamic panel model to commercial banks in the Association of South East Asian Nations over the period of 1999–2014. The results indicate that the higher profits in concentrated banking industries are partially attributable to the anti-competitive conduct of banks. These findings are robust across alternative measures of market structure and bank conduct, and different time horizons. The implications of these findings require regulators to make sure that the consolidation policy for ASEAN is achieving its purpose – i.e. financial stability – and not allowing the banks to earn monopoly rents.     

Is this what depression looks like? Visual narratives of depression on social media

Humans are inherent storytellers that in turn shapes our identity. The tendency to talk about health and illness is called ‘illness narratives.’ Most research on illness narratives is generated from diaries, interviews, and surveys. However, text is not the only means of expression. An alternative is to produce images of illness, known as ‘visual illness narratives.’ Depression is a common and serious illness, which can lead to serious health hazards. Despite the increased use of images on social media, little research has been done on visual narratives of depression. The current study examines such illness narratives elicited through images on Tumblr. Fourteen patients with moderately severe depression (Mean age = 27), mostly female (N = 12) were recruited from a counselling centre. Participants were presented with 72 depression-related images from Tumblr in a semi-structured interview. Participants sorted the images into three piles, based on personal relevance, and then talked in detail about each image. Inductive thematic analysis identified six themes providing rich insights into participants’ depressive states, dysfunctional thoughts, suicidal ideation, lifestyle changes, help-seeking, and social struggles. The study offers insights to improve care for depressed individuals using visuals of depression on social media.     

Growth Effects of Religion Dependent Social Capital: An Empirical Evidence

Social Indicators Research - There is an imperceptible laxity in literature in finding socio economic effects of social capital by integrating role of religion. However, international surveys data...     

A power log-Dagum distribution: estimation and applications

Development and application of probability models in data analysis are of major importance for all sciences. Therefore, we introduce a new model called a power log-Dagum distribution defined on the entire real line. The model contains many new sub-models: power logistic, linear log-Dagum, linear logistic and log-Dagum distributions among them. Some properties of the model including three different estimation procedures are justified. The model exhibits various shapes for the density and hazard rate functions. Moreover, the estimation procedures are compared using simulation studies. Finally, the model with others are fitted to three data sets, and it shows a better fit than the compared distributions defined on the real line.     

Wellbeing Matters in Kuwait: The Alnowair’s Bareec Education Initiative

Social Indicators Research - Wellbeing is predictive of improved learning outcomes, better mental health, prosocial and civic behavior, higher work productivity, and enhanced life satisfaction....     

Attribute control chart for some popular distributions

The binomial distribution is often used to display attribute control data. In this paper, a statistical model is settled for attribute control chart under truncated life test. By Burr X & XII, inverse Gaussian (IG), and exponential lifetime-truncated distributions, a Shewhart-type attribute control chart is built to display the data. The performance of attributed control chart constructed on truncated life test is evaluated by average run length, which compares the performance of all distributions. Our study arranges that IG is better distribution among all.     

Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies

In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.     

A Simple Method to Ensure Plausible Multiple Imputation for Continuous Multivariate Data

Multiple Imputation (MI) is an established approach for handling missing values. We show that MI for continuous data under the multivariate normal assumption is susceptible to generating implausible values. Our proposed remedy, is to: (1) transform the observed data into quantiles of the standard normal distribution; (2) obtain a functional relationship between the observed data and it's corresponding standard normal quantiles; (3) undertake MI using the quantiles produced in step 1; and finally, (4) use the functional relationship to transform the imputations into their original domain. In conclusion, our approach safeguards MI from imputing implausible values.     

Best linear unbiased and invariant estimation in location-scale families based on double-ranked set sampling

Abstract

In this article, we propose the best linear unbiased estimators (BLUEs) and best linear invariant estimators (BLIEs) for the unknown parameters of location-scale family of distributions based on double-ranked set sampling (DRSS) using perfect and imperfect rankings. These estimators are then compared with the BLUEs and BLIEs based on ranked set sampling (RSS). It is shown that under perfect ranking, the proposed estimators are uniformly better than the BLUEs and BLIEs obtained via RSS. We also propose the best linear unbiased quantile (BLUQ) and the best linear invariant quantile (BLIQ) estimators for normal distribution under DRSS. It is observed that the proposed quantile estimators are more efficient than the BLUQ and BLIQ estimators based on RSS for both perfect and imperfect orderings.