scholarly journals Analysis of survey on menstrual disorder among teenagers using Gaussian copula model with graphical lasso prior

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248340
Author(s):  
Jiali Wang ◽  
Anton H. Westveld ◽  
A. H. Welsh ◽  
Melissa Parker ◽  
Bronwyn Loong
Keyword(s):  
Gaussian Copula ◽  
Copula Model ◽  
Data Set ◽  
Menstrual Disorder ◽  
Teenage Girls ◽  
Graphical Lasso ◽  
Cohort Differences ◽  
Menstrual Disturbance ◽  
Using Data ◽  
Delays In Diagnosis

A high prevalence of menstrual disturbance has been reported among teenage girls, and research shows that there are delays in diagnosis of endometriosis among young girls. Using data from the Menstrual Disorder of Teenagers Survey (administered in 2005 and 2016), we propose a Gaussian copula model with graphical lasso prior to identify cohort differences in menstrual characteristics and to predict endometriosis. The model includes random effects to account for clustering by school, and we use the extended rank likelihood copula model to handle variables of mixed-type. The graphical lasso prior shrinks the elements in the precision matrix of a Gaussian distribution to encourage a sparse graphical structure, where the level of shrinkage is adaptable based on the strength of the conditional associations among questions in the survey. Applying our proposed model to the menstrual disorder data set, we found that menstrual disturbance was more pronouncedly reported over a decade, and we found some empirical differences between those girls with higher risk of developing endometriosis and the general population.

scholarly journals Cohort Differences and Similarities in Women's Attitudes About Self and Aging

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 56-56
Author(s):  
Aurora Sherman ◽  
Jamila Bookwala
Keyword(s):  
Physical Aging ◽  
Control Beliefs ◽  
Data Set ◽  
Identity Maintenance ◽  
Cohort Differences ◽  
Using Data ◽  
Aging Attitudes ◽  
The Life Course ◽  
Self Objectification ◽  
The Impact

Abstract This panel focuses on four complementing and international views of women’s aging, with a special emphasis on cohort comparisons and using three different studies of women, with contrasting methodological frameworks. In so doing, we present evidence related to trends in social percepetions of aging, attitudes about aging and identity, and ideas about control and objectification. Dr. Newton presents data on older Canadian women showing the connection between physical aging and identity maintenance, using both qualitative and quantitative data and using the lifecourse perspective. Dr. Ryan, using data from the Health and Retirement Study to compare cohorts of women from the 2008 and 2018 HRS waves, reports cohort differences in negative self-perceptions of aging, and that both cohort and negative self-perfections are associated with life satisfaction, using the life course developmental framework. Ms. Tran compares younger and older cohorts of women on a measure of self-objectification, finding that the older cohort reported lower objectification, consistent with a selection, optimization, and compensation (SOC) model. Finally, Dr. Sherman, using the same data set as Ms. Tran, shows that control beliefs are associated with objectification, regardless of cohort, consistent with objectification theory predictions of consistency over time regarding the impact of objectification experiences. Dr. Jamila Bookwala will provide discussion of this group of papers.

Inference about binocular sensitivity and specificity of screening tests for paired organs

2019 ◽  
Vol 29 (7) ◽  
pp. 1950-1959
Author(s):  
Tsung-Shan Tsou ◽  
Wei-Cheng Hsiao
Keyword(s):  
Model Misspecification ◽  
Screening Tests ◽  
Gaussian Copula ◽  
Copula Model ◽  
Joint Models ◽  
Data Set ◽  
Numerical Studies ◽  
Binomial Models ◽  
Fellow Eyes ◽  
Joint Probabilities

Recently Perera et al. introduced two new binocular accuracy measures to evaluate diagnostic tests for paired organs. They adopted the Gaussian copula model to account for correlation between fellow eyes. As the measures are functions of several joint probabilities and due to the nature of the joint models, variations of the estimates for the two new measures were assessed via bootstrapping. We provide a different approach to inference about the two interesting and innovative measures. In our opinion, when patients are independent, the binomial models suffice for inference about the parameters of interest. Inference becomes simple and straightforward. We perform numerical studies and analyse the data set as of Perera et al. for illustration. Also, we investigate thru simulations the issue of robustness of the Gaussian copula and the binomial models under model misspecification.

Risk-based capital regulation revisited: evidence from the early 2000s

2015 ◽  
Vol 23 (2) ◽  
pp. 115-134 ◽  
Author(s):  
Thomas L. Hogan ◽  
Neil R. Meredith ◽  
Xuhao (Harry) Pan
Keyword(s):  
Design Methodology ◽  
Banking Regulation ◽  
Bank Performance ◽  
Capital Regulation ◽  
Data Set ◽  
Content Type ◽  
Future Performance ◽  
Capital Ratios ◽  
Using Data ◽  
Capital Regulations

Purpose – The purpose of this study is to replicate Avery and Berger’s (1991) analysis using data from 2001 through 2011. Although risk-based capital (RBC) regulation is a key component of US banking regulation, empirical evidence of the effectiveness of these regulations has been mixed. Among the first studies of RBC regulation, Avery and Berger (1991) provide evidence from data on US banks that new RBC regulations outperformed old capital regulations from 1982 through 1989. Design/methodology/approach – Using data from the Federal Reserve’s Call Reports, the authors compare banks’ capital ratios and RBC ratios to five measures of bank performance: income, standard deviation of income, non-performing loans, loan charge-offs and probability of failure. Findings – Consistent with Avery and Berger (1991), the authors find banks’ risk-weighted assets to be significant predictors of their future performance and that RBC ratios outperform regular capital ratios as predictors of risk. Originality/value – The study improves on Avery and Berger (1991) by using an updated data set from 2001 through 2011. The authors also discuss some potential limitations of this method of analysis.

Twitter users exhibited coping behaviours during the COVID-19 lockdown: an analysis of tweets using mixed methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruchi Mittal ◽  
Wasim Ahmed ◽  
Amit Mittal ◽  
Ishan Aggarwal
Keyword(s):  
Data Analysis ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Twitter Data ◽  
Long Run ◽  
The World ◽  
Twitter Users ◽  
The Government ◽  
Using Data

Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

Investigating the clinical usefulness of definitions of progression with 10-2 visual field

2021 ◽  
pp. bjophthalmol-2020-318188
Author(s):  
Shotaro Asano ◽  
Hiroshi Murata ◽  
Yuri Fujino ◽  
Takehiro Yamashita ◽  
Atsuya Miki ◽  
...  
Keyword(s):  
Visual Field ◽  
False Positive ◽  
False Positive Rate ◽  
Clinical Validity ◽  
Data Set ◽  
Humphrey Field Analyzer ◽  
Positive Rate ◽  
Using Data ◽  
Sensitivity Specificity ◽  
Higher Sensitivity

Background/AimTo investigate the clinical validity of the Guided Progression Analysis definition (GPAD) and cluster-based definition (CBD) with the Humphrey Field Analyzer 10-2 test in diagnosing glaucomatous visual field (VF) progression, and to introduce a novel definition with optimised specificity by combining the ‘any-location’ and ‘cluster-based’ approaches (hybrid definition).Methods64 400 stable glaucomatous VFs were simulated from 664 pairs of 10-2 tests (10 sets × 10 VF series × 664 eyes; data set 1). Using these simulated VFs, the specificity to detect progression and the effects of changing the parameters (number of test locations or consecutive VF tests, and percentile cut-off values) were investigated. The hybrid definition was designed as the combination where the specificity was closest to 95.0%. Subsequently, another 5000 actual glaucomatous 10-2 tests from 500 eyes (10 VFs each) were collected (data set 2), and their accuracy (sensitivity, specificity and false positive rate) and the time needed to detect VF progression were evaluated.ResultsThe specificity values calculated using data set 1 with GPAD and CBD were 99.6% and 99.8%. Using data set 2, the hybrid definition had a higher sensitivity than GPAD and CBD, without detriment to the specificity or false positive rate. The hybrid definition also detected progression significantly earlier than GPAD and CBD (at 3.1 years vs 4.2 years and 4.1 years, respectively).ConclusionsGPAD and CBD had specificities of 99.6% and 99.8%, respectively. A novel hybrid definition (with a specificity of 95.5%) had higher sensitivity and enabled earlier detection of progression.

scholarly journals A search for transiting planets in the β Pictoris system

2018 ◽  
Vol 615 ◽  
pp. A145 ◽  
Author(s):  
M. Mol Lous ◽  
E. Weenk ◽  
M. A. Kenworthy ◽  
K. Zwintz ◽  
R. Kuschnig
Keyword(s):  
Signal To Noise Ratio ◽  
Large Fraction ◽  
Young Star ◽  
High Signal ◽  
Data Set ◽  
Transiting Planets ◽  
Using Data ◽  
Exoplanet Systems ◽  
Transit Signals

Context. Transiting exoplanets provide an opportunity for the characterization of their atmospheres, and finding the brightest star in the sky with a transiting planet enables high signal-to-noise ratio observations. The Kepler satellite has detected over 365 multiple transiting exoplanet systems, a large fraction of which have nearly coplanar orbits. If one planet is seen to transit the star, then it is likely that other planets in the system will transit the star too. The bright (V = 3.86) star β Pictoris is a nearby young star with a debris disk and gas giant exoplanet, β Pictoris b, in a multi-decade orbit around it. Both the planet’s orbit and disk are almost edge-on to our line of sight. Aims. We carry out a search for any transiting planets in the β Pictoris system with orbits of less than 30 days that are coplanar with the planet β Pictoris b. Methods. We search for a planetary transit using data from the BRITE-Constellation nanosatellite BRITE-Heweliusz, analyzing the photometry using the Box-Fitting Least Squares Algorithm (BLS). The sensitivity of the method is verified by injection of artificial planetary transit signals using the Bad-Ass Transit Model cAlculatioN (BATMAN) code. Results. No planet was found in the BRITE-Constellation data set. We rule out planets larger than 0.6 RJ for periods of less than 5 days, larger than 0.75 RJ for periods of less than 10 days, and larger than 1.05 RJ for periods of less than 20 days.

Using Data Mining for Forecasting Data Management Needs

2008 ◽  
pp. 2088-2104
Author(s):  
Qingyu Zhang ◽  
Richard S. Segall
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Forest Cover ◽  
Human Lung Cancer ◽  
Production Environment ◽  
Data Set ◽  
Biotechnology Research ◽  
Use Of Data ◽  
Using Data ◽  
Tools And Techniques

This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of data mining with multidimensional databases for determining data management needs for the selected biotechnology data of forest cover data (63,377 rows and 54 attributes) and human lung cancer data set (12,600 rows of transcript sequences and 156 columns of gene types). The data mining is performed using four selected software of SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and Bio- Discovery GeneSight®. The analysis and results will be used to enhance the intelligence capabilities of biotechnology research by improving data visualization and forecasting for organizations. The tools and techniques discussed here can be representative of those applicable in a typical manufacturing and production environment. Screen shots of each of the four selected software are presented, as are conclusions and future directions.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

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