Sample Mean Research Articles

Dishonesty is linked with the spread of infectious diseases

This paper investigates the link between dishonesty and the spread of COVID-19 infections. In an online experiment and panel survey, 2,723 Germans completed an incentivized coin-tossing task in March 2020 and reported their infection status in four subsequent survey waves up until December 2021. We find that individuals who are most likely dishonest in the coin-tossing task at the onset of the pandemic, as they report the highest number of winning coin tosses, are more than twice as likely to get a future COVID-19 infection than the sample mean. Respondents who are most likely to have reported dishonestly also engage more in behaviors that increase the risk of becoming infected and of transmitting the infection relative to likely honest respondents. Hence, we postulate that differences in preferences and norm compliance are underlying determinants that affect behavior in the experiment and in the field. We observe a similar relationship at the country level between an incentivized measure of civic honesty and excess deaths due to COVID-19 in 22 OECD countries.

Wasserstein Discriminant Dictionary Learning for Graph Representation.

Mining discriminative graph topological information plays an important role in promoting graph representation ability. However, it suffers from two main issues: (1) the difficulty/complexity of computing global inter-class/intra-class scatters, commonly related to mean and covariance of graph samples, for discriminant learning; (2) the huge complexity and variety of graph topological structure that is rather challenging to robustly characterize. In this paper, we propose the Wasserstein Discriminant Dictionary Learning (WDDL) framework to achieve discriminant learning on graphs with robust graph topology modeling, and hence facilitate graph-based pattern analysis tasks. Considering the difficulty of calculating global inter-class/intra-class scatters, a reference set of graphs (aka graph dictionary) is first constructed by generating representative graph samples (aka graph keys) with expressive topological structure. Then, a Wasserstein Graph Representation (WGR) process is proposed to project input graphs into a succinct dictionary space through the graph dictionary lookup. To further achieve discriminant graph learning, a Wasserstein discriminant loss (WD-loss) is defined on the graph dictionary, in which the graph keys are optimizable, to make the intra-class keys more compact and inter-class keys more dispersed. Hence, the calculation of global Wasserstein metric (W-metric) centers can be bypassed. For sophisticated topology mining in the WGR process, a joint-Wasserstein graph embedding module is constructed to model both between-node and between-edge relationships across inputs and graph keys by encapsulating both the Wasserstein metric (between cross-graph nodes) and proposed novel Kron-Gromov-Wasserstein (KGW) metric (between cross-graph adjacencies). Specifically, the KGW-metric comprehensively characterizes the cross-graph connection patterns with the Kronecker operation, then adaptively captures those salient patterns through connection pooling. To evaluate the proposed framework, we study two graph-based pattern analysis problems, i.e. graph classification and cross-modal retrieval, with the graph dictionary flexibly adjusted to cater to these two tasks. Extensive experiments are conducted to comprehensively compare with existing advanced methods, as well as dissect the critical component of our proposed architecture. The experimental results validate the effectiveness of the WDDL framework.

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model

The sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this condition is never met. As a result, in high dimensions the Ensemble Kalman Filter’s (EnKF) ensemble does not contain enough information to specify the prior covariance matrix accurately. This necessitates the need for regularization of the analysis (observation update) problem. We propose a regularization technique based on a new spatial model. The model is a constrained version of the general Gaussian process convolution model. The constraints include local stationarity and smoothness of local spectra. We regularize EnKF by postulating that its prior covariances obey the spatial model. Placing a hyperprior distribution on the model parameters and using the likelihood of the prior ensemble data allows for an optimized use of both the ensemble and the hyperprior. A linear version of the respective estimator is shown to be consistent. A more accurate nonlinear neural-Bayes implementation of the estimator is developed. In simulation experiments, the new technique led to substantially better EnKF performance than several existing techniques.

Alternative archives: Researching politics with chunks of reality

The challenges and opportunities of accessing empirical material present a puzzle insofar as they index simultaneously the politics of research archives and the positionality of researchers. Today, amid increasing authoritarianism, disinformation, and the obstacles created by the securitisation and privatisation of data, access to reliable information is frequently blocked or complicated by the interests of political management, corporate monopoly, and surveillance capitalism, as well as by institutional and personal constraints on researchers. At the same time, digital archives, social media, blogs, diverse global communications, and even the information management tools of authoritarian and corporate control, create innumerable alternative access points for research. In this article, we describe all of these other access points as ‘alternative archives’, and, in doing so, we also seek to highlight three overlapping definitions of their alternative status in terms of (1) empirical, (2) counter-hegemonic, and (3) epistemic meanings. We next provide five state-of-the-art examples of overcoming research obstacles due to state-of-the-world developments in surveillance capitalism, securitisation, authoritarianism, information control, and human insecurity, suggesting that we can learn from obstacles to research as well as the alternatives.

A New Modified Confidence Interval Estimator of Location Parameter for Skewed Distribution

A new modified confidence interval has been proposed which incorporates a new point estimator of the location parameter mean for skewed data distribution. Traditionally, the trimmed mean confidence interval estimator (Trm-ci) is a robust method for dealing with the skewness of the underlying data distribution. However, the Trm-ci method trims a certain fraction of endpoint observations to address skewness, which may result in a loss data information. The Students’t confidence interval (t-ci) estimator, while the most efficient estimator at normal models, becomes impractical in a situation where observed data is subject to non-normality due to robustness. In between the two, the median confidence interval estimator (Med-ci) is expected to retain the robustness of Trm-ci and the efficiency of t-ci. The idea behind the proposed new modified confidence interval estimator (Mod-ci) is to consider both the sample mean and sample median simultaneously, while also using end-point information without trimming any observations. As such, the proposed Mod-ci is expected to be as good as or better than other underlying methods regarding robustness and efficiency when dealing with skewed data distributions. In this study, we examine the performance of the new method compared to most commonly used methods through examples and simulating data from skewed distribution with varying degree of skewness. The results of examples and simulations suggest that the proposed method is as good as, or better than other estimators relevant to this study, as measured by estimated coverage probability and the width of associated confidence interval. Therefore, this method is recommended for practical application while dealing with real-life data exhibiting skewness.

Atkinson Index Detector for Spectrum Sensing

Recently, a new approach for designing detectors for cooperative spectrum sensing has been coined. It applies an incoming inequality index, which is normally used in economic and social sciences, to build a test statistic that operates on the entries of the received signal sample covariance matrix. The first detector designed using this approach was the Gini index detector (GID). This letter devises the Atkinson index detector (AID), which is shown to outperform the GID in many situations, with the special attribute of attaining a time complexity that grows linearly with the number of secondary users.

Prevalence and determinants of poor sleep quality among patients of diabetes mellitus: A tertiary hospital based cross sectional study

Background: The effects of diabetes on sleep quality are pronounced and have a cause-effect relationship. The major aim of this study was to check for the prevalence of poor sleep quality and associated factors among patients of diabetes mellitus in Pakistan as every 1 in 4th individual is suffering from diabetes in Pakistan. Materials and Methods: A hospital based cross-sectional study was conducted on diagnosed patients of diabetes mellitus from September 2023 to March 2024 using Pittsburgh Sleep Quality Index (PSQI). SPSS v.27 was used for data analysis. Frequencies, percentages for sample characteristics and means for PSQI were calculated. Chi-square and spearman were run for association and correlations respectively. Risk factors were predicted using binary logistic regression. Results: 384 responses were collected with 12 excluded. The prevalence of poor sleepers came out to be 82% and poor sleep was significantly associated and correlated with increasing age, poor glycemic control, and metformin as well as income, education and duration of diabetes respectively. Presence of comorbidities and uncontrolled diabetes were the only associated risk factors. Conclusions: Pakistani population with diabetes mellitus have an extremely high rate of poor sleep quality which should be addressed by adding sleep quality-oriented guidelines in the management of diabetes.

Extending Cochran’s Sample Size Rule to Stratified Simple Random Sampling with Applications to Audit Sampling

Audit samples are selected by businesses, institutions, government agencies, and other organizations to check the accuracy of financial reports and assess the quality of services provided among other reasons. A standard design used in auditing is stratified simple random sampling. Point estimates and confidence intervals for population values are usual products of an audit. Cochran’s sample size rule and its extension by Sugden, Smith, and Jones provided minimum sample size formulas for a simple random sample to ensure effective normal approximation and adequate coverage for nominal 95% confidence intervals of a standardized or Studentized sample mean. The purpose of this paper is to extend Cochran’s rule and establish a formula for the minimum sample size for the normal approximation and the use of traditional one-sided or two-sided confidence intervals to be acceptable for mean estimation in stratified simple random samples. We concentrate on variables that are at least partially continuous. Simulations are used to examine the performance of our sample size formula with a variety of skewed populations based on ones encountered in auditing.

THE IN-CONTROL PERFORMANCE OF THE ROBUST MULTIVARIATE SYNTHETIC CONTROL CHARTS FOR MONITORING MEAN SHIFT

Multivariate synthetic control chart enables monitoring of multiple process variable simultaneously and hence, negate the inflation of false alarm rates as can be seen in an individual statistical chart. A robust version of the synthetic chart is necessary to mitigate the issue faces by the traditional multivariate synthetic chart, which is unable to produce reliable parameter estimates when Phase I data are contaminated. Therefore, this study proposed three new robust multivariate synthetic control charts (CMRCD, CWS and CWP) which were constructed via minimum regularized covariance determinant (MRCD) and winsorized modified one-step M-estimator (WMOM) for efficient process monitoring. The effectiveness of the proposed robust charts was evaluated in terms of false alarm rates by comparing their in-control performances to the traditional multivariate synthetic chart, Cmean, which is based on the sample mean. Via extensive simulation studies, the findings indicate that the proposed robust control charts outperform the traditional chart regardless of the dimensions or the level of contaminations in the dataset. The real data study further validates that the CMRCD, CWS and CWP perform better than the Cmean. Specifically, the three robust control charts show significant observations, illustrating better capability in monitoring river water quality when compared to the Cmean, i.e., the traditional multivariate synthetic chart.

Development of Understanding the Concept of Opportunity Through Kulibia Game with Teaching at the Right Level (TaRL) Approach

Learning mathematics with a mechanistic approach results in being difficult for students to understand. Opportunity, as one of the mathematics contents, requires a learning process, which involves hands-on activities in teaching. Students were grouped according to their abilities based on the differences in students’ understanding through diagnostic assessment in the concept of differentiated learning, and through differentiation of content. Teaching at the right level (TaRL) is a differentiated learning technique, considered appropriate, to be applied in an effort to control student activities by utilizing Kulibia media as a learning context. Kulibia is one of the marine biota usually found by people on the coast and is used as one of the traditional games for children in Maluku. Through this game, students’ understanding of the concept of chance can be developed. The participants of this study were 8th grade students in Phase D, a total of 28 students. The data analysis technique used was one sample mean difference test. The results of data analysis prove that H0 is accepted meaning that the average understanding of the concept of chance exceeds the minimum completeness criteria of 70. Analysis of understanding shows that high-group students have a better understanding than students in other groups. Keywords: comprehension, Kulibia, TaRL, differentiated learning

A shrinkage based diagonal T 2 control chart for high dimensional data

Multivariate control charts are commonly used in manufacturing engineering to identify abnormal changes in the process. In a high-dimensional paradigm, where the number of variables (p) exceeds the number of Phase I subgroups (m), the sample covariance matrix is ill-conditioned and will become singular. This situation makes the classical T 2 control chart inefficient and even invalid to employ for monitoring a mean vector in statistical process control. This study proposes a new multivariate shrinkage-based diagonal T 2 control chart for high dimensional data, where p is very large compared to m with individual observation. A shrinkage estimator is used to estimate a diagonal covariance matrix to obtain an invertible, well-conditioned, and efficient estimate of the sample covariance matrix. The proposal's performance is also evaluated using the probability of detecting a shift. A simulation study reveals that the proposed control chart has an efficient sensitivity in detecting shifts for the high dimensional data. Results also show that m has a minor effect on the probability of shift detection for out-of-control data. In general, this probability improves for larger p, when shift is introduced in all p.

Analysis of The Effect of Three-Month DCA Accuracy on Optimistic Bias In Fulfilling Cooking Oil For DCA Recipient Families

Cooking oil is one of the basic needs for the people of Indonesia and is included in the nine staples. Currently, there is an increase in the price of cooking oil as a result of the price of crude palm oil on the international market increasing and reaching the highest price ever recorded in history. So that the government’s action to overcome the increase in cooking oil prices is to provide Direct Cash Assistance in the form of electronic money or non-cash assistance with the main target being Families Receiving Non-Cash Food Assistance and Family Hope Program and Street Vendors who sell fried food. This study aims to see the bias that occurs against DCA given to beneficiary families and provide suggestions for the government based on the results of cost accuracy in fulfilling cooking oil for 3 months. The method used in this research is the statistical test of single sample mean and double sample mean to determine the accuracy of respondents’ answers to the movement of cooking oil prices. The results showed that there was an optimistic bias towards the estimation of cooking oil prices by the community. People expect the price of cooking oil to fall, but in actual conditions the price of cooking oil for a month from May to June shows a fixed price neither rising nor falling.

Psychological testing in the profession of psychology: an Australian study

ABSTRACT Objectives In Australia, psychological measurement and testing is a core competency for all registered psychologists. This study aimed to provide a current perspective on the views of Australian psychologists in the use of psychological testing given the lack of recent Australian research. Method Psychologists (N = 821) completed online a demographic form and the European Federation of Psychologists’ Associations (EFPA) questionnaire on Test Attitudes of Psychologists – Modified (EQTAP-M), refined for the Australian setting. Constructs addressed in the survey included test appreciation, training, technology-based testing, attitudes to test use, and self-rated competence. The sample mean age was 50 years, with 84% over 34 years and 20% being males. Results Attitudinal differences, based on demographics and practice endorsement area, were examined. Partial support was obtained for the factorial model of previous EFPA studies. There was no difference in attitudes based upon the gender of the psychologists, but older psychologists reported less favourable attitudes and lower competence for psychological testing. Psychologists holding endorsements in organisational psychology, neuropsychology, and educational and developmental psychology indicated more positive testing attitudes and appreciation than other areas of practice. The application of technology in assessment was identified as an area that warrants further investigation and training. Conclusions The outcome has theoretical and practical implications for professional bodies and educational institutions that develop policies and training programs relevant to psychological measurement and testing.

A DEVICE-BASED (GAITON) SAGITTAL PLANE KINEMATICS ANALYSIS OF LOWER EXTREMITY JOINTS AMONG PATIENTS WITH KNEE OSTEOARTHRITIS – A METHODOLOGICAL APPROACH.

Background: Gait assessment is an effective method for identifying functional limitations associated with knee Osteoarthritis and understanding the relationship between disease symptoms and gait quality. Objective: To evaluate the mean difference in the kinematics of lower extremity joints among patients with knee osteoarthritis. Methods: A total of 20 people living with Knee OA were recruited as of 25th September 2024 in this prospective cross-sectional design study. The eligible participants were conveniently recruited from the SRM physiotherapy OPD based on voluntary participation after signing the informed consent. Adult participants aged 18 years and above, both sexes and able to walk independently without mobility aids were recruited and the joint kinematics were measured using the GaitOn Software, 2-dimensional analysis, and sagittal kinematic parameters of stance phase. Results: The mean age of the patients was 53.6 ± 7.3, and the mean BMI was 29.1±4.6. The one sample t-test demonstrated a statistically significant difference between kinematic angles of the lower extremity joint in sagittal plane of population mean (reference value) and sample mean (measured by GaitOn); Initial contact phase (hip: mean diff -4.06, p<0.01, ankle: mean diff 34.1, p < 0.0001), Loading response (hip: mean diff -5.8. p <0.001, knee: mean diff 9.6, p < 0.001, ankle: mean diff 32.4, p < 0.001). Midstance (hip: mean diff 11.06, p <0.001, ankle: 31.4, p< 0.001). Terminal stance (hip: mean diff 28.3, p< 0.004, ankle: mean diff 31.4, p< 0.001) and Pre-swing (hip: mean diff 16.5, p< 0.001, ankle: mean diff 31.4, p< 0.001). Conclusions: The hip and ankle joints equally share knee kinematic compensations. By acknowledging the complex interactions between the hip, ankle, and knee joints, healthcare providers can develop more comprehensive and effective treatment plans for individuals with knee OA.

Primary School Teachers’ Attitudes, Perceptions and Self-Efficacy Regarding Students With Special Educational Needs: A Comparative Study of the Education Systems in Slovenia and Kosovo

The purpose of this paper is to explore teachers’ attitudes, perceptions and self-efficacy regarding students with special educational needs in Slovenia and Kosovo, as well as to identify the similarities and differences between the two education systems in terms of inclusive practices. Quantitative data were collected from a sample of teachers working in grades 1–5 of primary schools in Slovenia (n = 258) and Kosovo (n = 255). The data were gathered using the Sentiments, Attitudes and Concerns about Inclusive Education scale and the Teacher Efficacy for Inclusive Practice scale and analysed using SPSS version 21.0. The findings indicate higher means of samples in both Slovenia and Kosovo in negative sentiments, attitudes and perceptions regarding inclusive education, while statistically significant differences were identified in attitudes and other factors, such as age, gender or teaching experience with students with special educational needs. Teachers in Slovenia and Kosovo scored higher in self-efficacy, which is negatively correlated at a significant level with sentiments, attitudes and perceptions regarding inclusive education.

Incidence and Risk Factors for Amputations in Persons with Diabetes Mellitus: A Retrospective Cohort Study.

The aim of this study was to identify and analyze the incidence rate of amputations and their risk factors in people with Diabetes Mellitus (DM) in two specialized outpatient clinics in Brazil. This is an epidemiological, retrospective cohort study using data collected from electronic health records of 281 adult diabetic patient types 1 or 2; attended in specialized outpatient service between 2015 and 2020. Statistical analyses were performed using the 2 sample t-test or Wilcoxon-Mann-Whitney test, for quantitative variables, and the Pearson's χ2 test or Fisher's exact test for categorical variables. The investigation of the risk factors for amputation was carried out through logistic regression. The study was approved by ethical committee. The sample mean age was 65.6 years (SD 13.05), predominating male gender n = 211 (75%), type 2 DM n = 223 (86.7%), with cardiovascular disease n = 143 (63.2%), and about 68.7% (n = 156) with peripheral arterial disease (PAD). Seventy-seven had lower limb amputation (LLA), with a rate incidence of 31.9% during five years. Logistic regression analysis showed the following associations with amputation: Diabetic peripheral neuropathy increased the rate of amputation by 3.6 times (OR = 3.631, 95% CI = 1.214-11.353; P = .022), and peripheral arterial disease increased by 10 times (OR = 10.631; 95% CI = 2.969-57.029; P = .001). The LLA in individuals with DM in two specialized outpatient services was higher compared to international literature; DPN and PAD were confirmed as risk factors for amputation, according to literature. This finding suggests that the study population faces an increased risk of amputation, highlighting the urgent need for targeted interventions and implementing robust preventive strategies to transform the current scenario and mitigate these severe outcomes. A comprehensive approach is essential to proactively address the underlying issues and reduce the prevalence and impact of amputations in Brazil.

Great Year Bad Sharpe

Returns distributions are heavy tailed across asset classes. In this note, Smerlak examines the implications of this well-known stylized fact for the joint statistics of performance (absolute return) and Sharpe ratio (risk-adjusted return). Using both synthetic and real data, He shows that, all other things being equal, the investments with the best in-sample performance are never associated with the best in-sample Sharpe ratios (and vice versa). This counter-intuitive effect is unrelated to the risk-return tradeoff familiar from portfolio theory: it is, rather, a consequence of asymptotic correlations between the sample mean and sample standard deviation of heavy-tailed variables. In addition to its large sample noise, this non-monotonic association of the Sharpe ratio with performance puts into question its status as the gold standard metric of investment quality.

A pharmacovigilance analysis of post-marketing safety of tezepelumab

BackgroundTezepelumab has shown promising efficacy for adult patients with severe asthma since its approval. However, the post-marketing safety evaluation of tezepelumab is currently lacking. ObjectiveThe present study aims to investigate the post-marketing safety of tezepelumab based on the FDA Adverse Event Reporting System (FAERS) database. MethodsAdverse events (AEs) reports from January 2022 to December 2023 were extracted from the FAERS database. Disproportionality analysis by reporting odds ratio (ROR) and empirical Bayesian geometric mean (EBGM) was performed to detect potential AEs related to tezepelumab. Clinical characteristics and time to onset of AEs were also assessed. ResultsA total of 1,699 tezepelumab-related AE reports were identified during the study period. Thirty tezepelumab-related AE signals were detected by simultaneously applying the two algorithms. At the system organ class (SOC) level, the most common SOC related to tezepelumab was respiratory, thoracic and mediastinal disorders. At the preferred term (PT) level, common AEs including arthralgia and back pain were detected which were also documented in the label of tezepelumab and clinical trials. New unexpected AEs such as chest pain and myalgia were also identified. The median time to onset of tezepelumab-related AEs was 7.5 days, and the majority of AEs occurred within the first 1 month after tezepelumab initiation. ConclusionThe present study presents a comprehensive evaluation of the post-marketing safety of tezepelumab in the real-world setting. Our findings will provide valuable evidence for future clinical studies and management of safety issues of tezepelumab.

Comprehensive evaluation of leuprorelin-associated adverse events: insights from FDA adverse event reporting system

ABSTRACT Background Leuprorelin, a gonadotropin-releasing hormone agonist, is widely used to treat hormone-related disorders. This study aims to explore and analyze the safety profile of leuprorelin by examining adverse event reports from the FDA Adverse Event Reporting System (FAERS) database. Methods This study conducted a retrospective pharmacovigilance analysis using FAERS data from Q1 2004 to Q1 2024. Adverse drug events (ADEs) related to leuprorelin were identified and categorized by system organ class and specific adverse events. Statistical methods such as Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) were used to detect safety signals. Results A total of 63,928 ADE reports implicated leuprorelin, with 500 preferred terms and 25 system organ classes showing significant disproportionality. Notable rare ADEs identified were bulbospinal muscular atrophy congenital (n = 26; ROR 1282.72, PRR 1282.52, IC 7.91, EBGM 241.28), follicular cystitis (n = 3; ROR 126.84, PRR 126.84, IC 6.48, EBGM 89.09), and anaplastic meningioma (n = 3; ROR 46.73, PRR 46.73, IC 5.34, EBGM 40.5). Conclusion Most findings were expected, but new signals like follicular cystitis, previously unreported, emerged. Further studies are essential to validate these findings, crucial for clinical monitoring and risk identification of leuprorelin.

Two sample test for covariance matrices in ultra-high dimension

In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of the two samples. Our proposed methodology relies on a data splitting procedure and a comparison of a set of well selected eigenvalues of the sample covariance matrices on the split data sets. Compared to the existing methods, our methodology is adaptive in the sense that (i). it does not require specific assumption (e.g., comparable or balancing, etc.) on the sizes of two samples; (ii). it does not need quantitative or structural assumptions of the population covariance matrices; (iii). it does not need the parametric distributions or the detailed knowledge of the moments of the two populations. Theoretically, we establish the asymptotic distributions of the statistics used in our method and conduct the power analysis. We justify that our method is powerful under weak alternatives. We conduct extensive numerical simulations and show that our method significantly outperforms the existing ones both in terms of size and power. Analysis of two real data sets is also carried out to demonstrate the usefulness and superior performance of our proposed methodology. An R package UHDtst is developed for easy implementation of our proposed methodology.