Dr. Achraf Cohen

This comprehensive study delves into the examination and application of novel statistical distributions, namely the Ristić-Balakrishnan-Topp-Leone-Exponentiated half Logistic-G (RB-TL-EHL-G) family of distributions, emphasizing their paramount importance in reliability and medical data modeling. We meticulously explore a multitude of this family of novel distributions, accentuating their respective features, properties, and real-world applicability. The probability density, the cumulative distribution, the hazard rate, and the quantile functions are provided. The density functions of the RB-TL-EHL-G family are expanded, enabling a deeper understanding of their statistical properties, including various moments, generating functions, order statistics, stochastic orderings, probability weighted moments, and the Rényi entropy. A significant portion of the investigation is dedicated to the intensive analysis of various data sets, to which these distributions are fitted, unveiling noteworthy insights into their behavior and performance. Furthermore, the discussions extend to a comparative study, delineating the advantages and limitations of each distribution, fostering a deeper understanding and selection criteria for practitioners.

This paper presents a new methodology for generating continuous statistical distributions, integrating the exponentiated odds ratio within the framework of survival analysis. This new method enhances the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets. The core of this advancement is illustrated by introducing a particular subfamily, the “Type 2 Gumbel Weibull-G family of distributions”. We provide a comprehensive analysis of the mathematical properties of these distributions, including statistical properties such as density functions, moments, hazard rate and quantile functions, Rényi entropy, order statistics, and the concept of stochastic ordering. To test the robustness of our new model, we apply five distinct methods for parameter estimation. The practical applicability of the Type 2 Gumbel Weibull-G distributions is further supported through the analysis of three real-world datasets. These real-life applications illustrate the exceptional statistical precision of our distributions compared to existing models, thereby reinforcing their significant value in both theoretical and practical statistical applications.

In this paper, we investigate the waiving of GRE/GMAT for admission to master’s degree programs in a state university in Florida, USA. Standardized tests, such as GRE/GMAT, were required for admission to the master’s degree programs in 2019/2020, waived in 2020/2021, and removed or modified in 2021/2022. We analyzed the application, enrollment, and performance data to assess the impact of these changes. The data show that the number of applicants and enrolled students exhibit an upward trend from 2019 to 2021. The undergraduate GPA of new applicants who did not submit the GRE in 2021 tends to be statistically significantly higher than for those who did submit the GRE in 2019 (p < 0.001). The new students’ first-semester graduate GPA in 2021 (no GRE requirement) tends also to be statistically significantly higher than the new students’ first-semester graduate GPA in 2019 (GRE requirement) (p< 0.01). The study employed random forest feature importance using the Gini index to analyze the predictive power of GRE and undergraduate GPA for forecasting first-semester graduate GPA. The results show that undergraduate GPA is a more significant factor than GRE. Overall, the study’s statistical evidence indicates that waiving GRE/GMAT requirements for master’s degree programs did not affect applicants’ performance, as measured by their undergraduate GPA, nor did it lead to a decline in student performance, as measured by first-semester graduate GPA.

The objective of this study was to evaluate the impact of three different methods of handling missing data on the performance of Phase I Hotelling’s T² multivariate control chart. Using a Monte Carlo simulation, we studied the average, median, and standard deviation of the run length performance of multivariate data imputed using mean substitution, regression imputation, and predictive mean matching at three different levels of missingness (1%, 10%, and 25%) and three levels of variable correlation coefficients (0.2, 0.4, and 0.8). We found that predictive mean matching has average run length performance results comparable to that of the complete in-control data set at all levels of missingness and variable correlation, while the performance of mean substitution was adversely affected by high levels of missingness and by strong variable correlation. Based on the simulation (multivariate normal data), we concluded that predictive mean matching is superior to both regression imputation and mean substitution as a method for imputing missing values for the analysis of Phase I Hotelling’s T² control chart. Two applications were presented using the Altenrhein wastewater treatment plant and Olive oil datasets.

This paper introduces the Lomax-exponentiated odds ratio–G (L-EOR–G) distribution, a novel framework designed to adeptly navigate the complexities of modern datasets. It blends theoretical rigor with practical application to surpass the limitations of traditional models in capturing complex data attributes such as heavy tails, shaped curves, and multimodality. Through a comprehensive examination of its theoretical foundations and empirical data analysis, this study lays down a systematic theoretical framework by detailing its statistical properties and validates the distribution’s efficacy and robustness in parameter estimation via Monte Carlo simulations. Empirical evidence from real-world datasets further demonstrates the distribution’s superior modeling capabilities, supported by compelling various goodness-of-fit tests. The convergence of theoretical precision and practical utility heralds the L-EOR–G distribution as a groundbreaking advancement in statistical modeling, significantly enhancing precision and adaptability. The new model not only addresses a critical need within statistical modeling but also opens avenues for future research, including the development of more sophisticated estimation methods and the adaptation of the model for various data types, thereby promising to refine statistical analysis and interpretation across a wide array of disciplines.

Background: It is theorized that the lack of a synovial lining after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) contributes to slow ligamentization and possible graft failure. Whether graft maturation and incorporation can be improved with the use of a scaffold requires investigation. Purpose: To evaluate the safety and efficacy of wrapping an ACL autograft with an amnion collagen matrix and injecting bone marrow aspirate concentrate (BMAC), quantify the cellular content of the BMAC samples, and assess 2-year postoperative patient-reported outcomes. Study Design: Randomized controlled trial; Level of evidence, 2. Methods: A total of 40 patients aged 18 to 35 years who were scheduled to undergo ACLR were enrolled in a prospective single-blinded randomized controlled trial with 2 arms based on graft type: bone–patellar tendon–bone (BTB; n = 20) or hamstring (HS; n = 20). Participants in each arm were randomized into a control group who underwent standard ACLR or an intervention group who had their grafts wrapped with an amnion collagen matrix during graft preparation, after which BMAC was injected under the wrap layers after implantation. Postoperative magnetic resonance imaging (MRI) mapping/processing yielded mean T2* relaxation time and graft volume values at 3, 6, 9, and 12 months. Participants completed the Single Assessment Numeric Evaluation Score, Knee injury and Osteoarthritis Outcome Score, and pain visual analog scale. Statistical linear mixed-effects models were used to quantify the effects over time and the differences between the control and intervention groups. Adverse events were also recorded. Results: No significant differences were found at any time point between the intervention and control groups for BTB T2* (95% CI, –1.89 to 0.63; P = .31), BTB graft volume (95% CI, –606 to 876.1; P = .71), HS T2* (95% CI, –2.17 to 0.39; P = .162), or HS graft volume (95% CI, –11,141.1 to 351.5; P = .28). No significant differences were observed between the intervention and control groups of either graft type on any patient-reported outcome measure. No adverse events were reported after a 2-year follow-up. Conclusion: In this pilot study, wrapping a graft with an amnion collagen matrix and injecting BMAC appeared safe. MRI T2* values and graft volume of the augmented ACL graft were not significantly different from that of controls, suggesting that the intervention did not result in improved graft maturation. Registration: NCT03294759 (ClinicalTrials.gov identifier).

To meet the ever-growing demand for higher data rates, accurate channel models are needed for optimal design and deployment of mobile wireless networks. This work proposes a new method for addressing path loss modeling at 800 MHz of suburban environment based on field measurements. Using generalized linear mixed models, we develop a new statistical model that accounts for the autocorrelation among measurements at the same distance at different times. The proposed method allows linear, quadratic, and cubic relationship forms between the path loss measurements and the natural logarithm of the distance, which is almost unexplored as existing models use a straight line relationship. A comparison study consists of comparing nine propagation models in terms of the mean absolute prediction error. The new model performs over 30% better than the existing models for the considered measurements. We also show that a cubic relationship form between path loss measurements and the logarithm of distance could be more suitable than a straight line form for prediction purposes. The results show that the generalized linear mixed models significantly improve the prediction power regardless of the form of the model (linear, quadratic, or cubic).

Sexual aggression and offense among youths have long been subjected to scientific inquiries. A sizable number of these inquiries have identified sexually aggressive youths to constitute a heterogeneous group with possible distinct subgroups varying across their targeted victims and risk factors. This study aims to contribute to the growing body of research geared toward identifying these subgroups. Using self-reported data collected from US middle and high school students, this study employs a latent class analysis (LCA) to identify the subgroups of sexually aggressive youths using a data-driven strategy. The LCA results indicate three latent classes (sub-groups) to be distinguishable both quantitatively and qualitatively in the study sample: general delinquents, emotionally disturbed, and low-risk youths. These subgroups are comparable with those identified in similar previous studies.
However, the subgroups’ sizes in this study vary slightly from the sizes of the subgroups identified by earlier studies. The study concludes by presenting the reasons for the identified differences, policy implications, study limitations, and future research directions

This study intended to explore possible variations among youth adjudicated for sexual offenses based on personal criminogenic factors, offense, and victim characteristics. Utilizing a data set collected from the juvenile court files in Turkey (n = 460), the Latent Class Analysis revealed that the study sample included three different subgroups with distinct features: "non-delinquent, peer victim-targeting," "non-delinquent, younger victim-targeting," and "delinquent, versatile" youth adjudicated for sex offenses. The first two of these groups were similar in terms of having low levels of delinquency, while the third group included the lowest number of youth with significantly broad delinquent activity patterns. These findings were in line with the results of previous studies, and the implications were discussed for future research and policy development.

Background: Ulnar collateral ligament (UCL) tears in the throwing elbow are classified according to grade and location using magnetic resonance arthrography (MRA). However, the frequency of each tear type and the association to age, competition level, and radiographic findings in adolescent baseball pitchers are unknown.
Purposes: The primary purpose of this study was to use MRA to characterize the severity, location, and UCL tear type in adolescent pitchers. The second aim was to describe the relationship between the UCL tear type and age, competition level, and plain radiographic findings.
Study Design: Cross-sectional study; Level of evidence, 3. Methods: Records of adolescent pitchers with a UCL tear treated by the senior author between 2007 and 2016 were retrospectively reviewed. MRA scans were reviewed and tears were classified according to the Joyner-Andrews classification. Low-grade partial tears are classified as type I, high-grade partial tears as type II, complete full-thickness tears as type III, and tear pathology in >1 region in the UCL as type IV. Each type of tear also has a location designated at the midsubstance, ulna (U), or humerus (H). Patient characteristics, competition level, and associated plain radiographic abnormalities were recorded. Univariate analyses were performed to examine the relationships between tear types and age, competition level, and plain radiographic findings.
Results: A total of 200 adolescent pitchers (mean +/- SD age, 17.2 +/- 1.5 years) with MRA scans were reviewed. Type II-H (n = 62), type II-U (n = 51), and type III-U (n = 28) were the most common tear types observed. Type II tears comprised 64.5% of adolescent UCL tears, with type II-H being the most common. Plain radiographs were abnormal in 32% of patients, with calcifications (10.5%) and olecranon osteophytes (12.5%) being the most common findings. There were no significant relationships between tear type and age (P = .25), competition level (P = .23), or radiographic abnormalities (P = .75).
Conclusion: Humeral-sided high-grade partial tears were the most common tear type in adolescent pitchers. There was no relationship between UCL tear type and age competition level, and plain radiographic abnormalities.