The Topp-Leone Extended Exponential Distribution: Estimation Methods and Applications

Al-Shomrani et al. (2016) introduced a new family of distributions (TL-G) based on the Topp-Leone distribution (TL) by replacing the variable x by any cumulative distribution function G(t). With only one extra parameter which controls the skewness, this family is a good competitor to several generalized distributions used in statistical analysis. In this work, we consider the extended exponential as the baseline distribution G to obtain a new model called the Topp-Leone extended exponential distribution TL-EE. After studying mathematical and statistical properties of this model, we propose different estimation methods such as maximum likelihood estimation, method of ordinary and weighted least squares, method of percentile, method of maximum product of spacing, method of Cramer Von-Mises, modified least squares estimators and chi-square minimum method for estimating the unknown parameters. In addition to the classical criteria for model selection, we develop for this distribution a goodness-of-fit statistic test based on a modification of Pearson statistic. The performances of the methods used are demonstrated by an extensive simulation study. With applications to covid-19 data and waiting times for bank service, a comparison evaluation shows that the proposed model describes data better than several competing distributions.

A New Extension of the Topp–Leone-Family of Models with Applications to Real Data

In this article, we proposed a new extension of the Topp–Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress–strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp–Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice;the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp–Leone's and exponential related distributions based on the real data illustrations.

Hedging and safe haven properties of Ethereum: evidence around crises

Given the broad scope of Ethereum and the wide range of its decentralized applications, this paper investigates its hedging and safe haven capabilities against main fiat currencies, stock and bond indices in the US and Europe, and crude oil and gold markets. We use daily data from January 2016 until February 2021 and apply percentile regressions and crisis event interaction analysis by selecting four worldwide events including US presidential elections, the Brexit referendum, and COVID-19. We reveal that Ethereum does not act as a hedge or a safe haven against fiat currencies, stock and bond indices, and gold. However, it does act as a strong safe haven against crude oil in calm and turbulent periods and against European bonds during market turbulence. The study provides insights to regulators and investors into the potential role of Ethereum in investment decision-making and protecting financial market participants in the US and EU.

Evaluation of the Relationship between the Weight and Height Percentiles and the Sequence and Chronology of Eruption in Permanent Dentition.

The standard eruption of the permanent dentition in growing patients is influenced by multiple environmental factors. The objective of this research was to study the relationship between height and weight percentiles and the eruption of the permanent dentition. The design of the study was transversal based on the review of the clinical history, visual dental inspection, weight and height indicators, and their respective percentiles in patients in the mixed and definitive dentition stage. The descriptive and comparative analysis of the data was carried out with the statistical software R version 4.1.1. The sample size was 725 participants. The mean age of eruption of the first tooth was 8.0. The eruption sequence in the upper arch was first molar, central and lateral incisor, first premolar, canine, second premolar, and second molar. In the lower arch, the eruption sequence was: central incisor, first molar, lateral incisor, canine, first and second premolar, and second molar. The most frequent weight percentile was P50-97 (50.34%) and height P3-50 (53.38%). Weight (0.0129; 0.0426; 0.0495; 0.000166) and height (0.00768; 0.00473; 0.00927; 10-5) variables significantly influenced dental eruption. The factor that most influences the eruption of the permanent dentition is the height percentile.

Lack of Association between Pandemic School Mode Policy and Pediatric Body Mass Index Percentile over Time.

During the 2020-2021 academic year, schools across the country were closed for prolonged periods. Prior research suggests that children tend to gain more weight during times of extended school closures, such as summer vacation; however, little is known about the impact of school learning mode on changes. Thus, the aim of this study was to measure the association between school mode (in-person, hybrid, remote) and pediatric body mass index (BMI) percentile increases over time. In this longitudinal, statewide retrospective cohort study in Massachusetts, we found that BMI percentile increased in elementary and middle school students in all learning modes, and that increases slowed but did not reverse following the statewide reopening. Body mass percentile increases were highest in elementary school aged children. Hispanic ethnicity and receipt of Medicaid insurance were also associated with increases. Additional research is needed to identify strategies to combat pediatric body mass percentile increases and to address disparities.

An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Impact of cash flow volatility and debt structure in context of COVID pandemic: A study in Vietnam

The study examines the impact of cash flow volatility on the debt structure of listed enterprises in Vietnam in period from 2009 to 2020. We use general least square method and percentile regression to examine this impact. Research results show that there exists an inverse relationship between cash flow volatility and corporate debt structure (financial leverage, short-term debt and long-term debt) of Vietnamese enterprises during the research period. In particular, unlike previous studies on cash flow volatility, this study further examines impact of the COVID pandemic on the relationship between cash flow volatility and debt structure. The test results show that cash flow volatility in the context of the COVID-19 pandemic has an positive impact on debt structure. At the same time, the study also finds that the cash flow volatility will decrease as the percentile of the debt structure increases.

DETERMINATION of HIGH-RISK MUNICIPALITY of COVID-19 CASE in AGUSAN DEL SUR, MINDANAO, PHILIPPINES USING GIS APPROACH

The greatest battle that Filipinos face today was the fight of Corona Virus Disease or the SARS-CoV 2. COVID-19 pandemic is life-threatening in terms of our public health. Many lives were killed, and some are survived this pandemic brought that. The typical living of Filipinos was turned into a catastrophic form of living because the enemy was unseen. The economic and public health sector was put to balances to ensure the general welfare of the public. Government efforts in battling this disease were extremely advantageous because public safety is of paramount importance at all. We come out with this study to mitigate, respond, and prioritize those areas with its riskiest for the mass. This study aims to determine and visualize the high-risk municipalities of Agusan del Sur using the method of min-max normalization and the percentile ranking by quartiles. Percentile Ranking is used to determine the range of classified as low, moderate, high, and very high. In determining the overall risk in the Province of Agusan del Sur, we used the Analytical Hierarchy Process, a multi-criteria decision-making platform. We chose the experts of this field to respond in the AHP Form. Pairwise comparison is being used in this study to determine each risk factor indicator;all of their ratings will be used in processing weighted overlay analysis in Arc GIS which is one of the major activities of this study. The study can aid the local government plan, and direct mitigation plans to suppress and preclude the COVID-19 pandemic. The result shows that the Municipality of Prosperidad was the very high-risk Municipality of Agusan del Sur of COVID-19. On the other hand, Bayugan, San Francisco, Talacogon, and Trento were placed at high risk. In contrast, Esperanza, San Luis, Lapaz, Rosario, Bunawan, Santa Josefa and Sibagat, Veruela, Loreto were placed under the category of moderate and low respectively. © ACRS 2021.All right reserved.

One-Class Classifier for Chest X-ray Anomaly Detection via Contrastive Patch-based Percentile

Given its low dose and compactness, chest radiography has been widely used as the first-line test to determine the presence of lung anomalies. Nevertheless, a high-performance diagnosis for initial screening to detect shadows in lungs due to general lung diseases is not available. During initial screening, chest radiography can be used to distinguish any diseased lung shadowing caused by lung diseases. Thus, chest radiography can contribute to the early diagnosis and prevention of novel lung infectious diseases if training for a specific disease is not required. Accordingly, we propose a deep-learning-based diagnostic system called contrast-shifted instances via patch-based percentile (CSIP) to automatically detect diseased lung shadowing via training only on chest X-ray data from healthy subjects. CSIP is the first application of a patch-based percentile approach to state-of-the-art one-class classifiers (OCCs). This application improves the sensitivity of the network to recognize shadowing density differences in each local area of the lung, thereby considerably improving the diagnostic performance of average area under the curve (AUC) by more than 20% and achieving a sufficiently high diagnostic performance (average AUC of 0.96 for various lung diseases), compared to the existing OCC case without applying our patch-based approach (average AUC of 0.74). Therefore, CSIP may contribute to the early detection of anomalies caused by novel infectious diseases such as variants of the coronavirus disease, for whom training data are scarce. The code is available at https://github.com/kskim-phd/CSIP. Author