Maximum Likelihood Research Articles

Neutrosophic Maxwell–Boltzmann Distribution: Properties and Application to Healthcare Data

In this work, we present and analyze new probability distribution by generalizing the classical Maxwell–Boltzmann model to neutrosophic structure. The generalized structure, known as the neutrosophic Maxwell (NMX) model that is designed to analyze data with imprecise or vague information. Closed-form expressions for cumulative distribution functions, probability density functions, survival functions, hazard functions, and moments, moment generating functions, mode, skewness, and kurtosis are derived as part of its detailed mathematical and statistical characteristics. The parameter estimation of the suggested model is carried out employing the maximum likelihood estimation (MLE) technique, and the statistical properties of the estimators are discussed in uncertain environments. The inverse cumulative distribution method is established to generate random samples from the proposed model and to evaluate the efficiency of the MLE method. Eventually, a real-world healthcare data set is used to show the efficacy of the proposed model. This research provides new knowledge in the field of neutrosophic statistics, laying a foundation for further exploration in this area

Multidimensional latent cognitive diagnosis based on rough concept analysis and item response theory

The rapid increase of the amount of learning resources has brought numerous challenges to personalized learning. Although various cognitive diagnosis models and path recommendation models have been developed, there exist many challenges, especially in the accuracy of diagnosing learners’ latent cognitive traits and efficiency of optimizing personalized learning paths. In this paper, we build a multidimensional cognitive diagnosis model by combining fuzzy set theory and item response theory. In particular, we use the maximum likelihood estimation method to assess learners’ cognitive state on knowledge/skills and learning resources. In addition, based on rough concept analysis and genetic algorithms, we develop a model of personalized learning path optimization. Finally, data experiments are conducted to demonstrate the efficiency of the proposed models.

Modeling of lifetime scenarios with non-monotonic failure rates.

The Weibull distribution is an important continuous distribution that is cardinal in reliability analysis and lifetime modeling. On the other hand, it has several limitations for practical applications, such as modeling lifetime scenarios with non-monotonic failure rates. However, accurate modeling of non-monotonic failure rates is essential for achieving more accurate predictions, better risk management, and informed decision-making in various domains where reliability and longevity are critical factors. For this reason, we introduce a new three parameter lifetime distribution-the Modified Kies Weibull distribution (MKWD)-that is able to model lifetime scenarios with non-monotonic failure rates. We analyze the statistical features of the MKWD, such as the quantile function, median, moments, mean, variance, skewness, kurtosis, coefficient of variation, index of dispersion, moment generating function, incomplete moments, conditional moments, Bonferroni, Lorenz, and Zenga curves, and order statistics. Various measures of uncertainty for the MKWD such as Rényi entropy, exponential entropy, Havrda and Charvat entropy, Arimoto entropy, Tsallis entropy, extropy, weighted extropy and residual extropy are computed. We discuss eight different parameter estimation methods and conduct a Monte Carlo simulation study to evaluate the performance of these different estimators. The simulation results show that the maximum likelihood method leads to the best results. The effectiveness of the newly suggested model is demonstrated through the examination of two different sets of real data. Regression analysis utilizing survival times data demonstrates that the MKWD model offers a superior match compared to other current distributions and regression models.

Psychometric assessment of scales used to evaluate sexual assault prevention programming in the United States Air Force.

Preventing sexual assault in the United States (U.S.) military is essential to safeguard the overall well-being of military personnel and support the military to function in alignment with its intended mission and objectives. Valid instruments are needed to accurately and reliably evaluate programming effectiveness. The goal of this research was to psychometrically assess measures used to evaluate the Sexual Communication and Consent (SCC) program within the Air Force Basic Military Training (BMT) context. We evaluated four measures used to assess the SCC program implemented at Air Force BMT in 2019-2020: Date Rape Attitudes, Self-Efficacy to Resist Unwanted Advances, Risky and Protective Dating Behaviors, and Bystander Intentions. The analytic sample included 7,126 BMT trainees (74% male). We assessed structural validity with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We used full information maximum likelihood estimation with robust standard errors in Mplus. We refined each scale based on factor analysis results and assessed internal consistency reliability by computing Cronbach's coefficient alpha for each scale in the overall sample, by sex, and by tailored SCC intervention group. We identified a two-factor structure for the Date Rape Attitudes scale; subscale reliability was moderate within the overall sample and among males, though low among females. We found single-factor structures and excellent reliability for both the Self-Efficacy to Resist Unwanted Advances scale and the Bystander Intentions scale. The Dating Behaviors scale CFA did not confirm the two-factor solution suggested by the EFA, but subscale reliability was acceptable. This research fills a critical gap in the psychometric literature in military settings. Based on our findings, we recommend approaches for using the finalized scales in future evaluations of sexual assault prevention programming in the U.S. Air Force BMT setting.

Modeling Extreme Healthcare Costs Using the Neutrosophic Cauchy Distribution

Real data modelling of extreme events, such as rainfall, temperature, financial costs is very important in neutrosophic statistical methods. The Cauchy distribution is one of statistical models used for modelling such extreme events in natural processes. In cases of imprecise data which most often involve vague, incomplete and ambiguous information, standard statistical methods cannot fully describe the spectrum of uncertainty. In this study, we have considered a new Cauchy distribution under neutrosophic context to deal with uncertain data. The proposed neutrosophic Cauchy distribution (NCD) may analysis extreme events data involving incomplete observations. We provide basic mathematical characteristics and important statistical functions of the Cauchy model under neutrosophic framework. A complete procedure of random numbers generation using neutrosophic quantile function is discussed. The unknown parameters of the proposed are estimated using the maximum likelihood approach. Numerical results show that the proposed model adequately fits the data involving extreme and imprecise values. The performance and flexibility of the model are also supported by an application to a real data set.

Analysis of Bilateral and Unilateral Data: A Comparative Review of Model-Based and MLE-Based Methods for the Homogeneity Test of Proportions

In many medical comparative studies, subjects may provide either bilateral or unilateral data. While numerous testing procedures have been proposed for bilateral data that account for the intra-class correlation between paired organs of the same individual, few studies have thoroughly explored combined correlated bilateral and unilateral data. Ma and Wang (2021) introduced three test procedures based on the maximum likelihood estimation (MLE) algorithm for general g groups. In this article, we employ a model-based approach that treats the measurements from both eyes of each subject as repeated observations. We then compare this approach with Ma and Wang’s Score test procedure. Monte Carlo simulations demonstrate that the MLE-based Score test offers certain advantages under specific conditions. However, this model-based method lacks an explicit form for the test statistic, limiting its potential for further development of an exact test.

Marshall-Olkin Power Hazard Rate Distribution with Associated Minification Process and Application

In this paper, a new three-parameter lifetime distribution of Marshall-Olkin family of distributions with the baseline model as power hazard rate distribution called the Marshall-Olkin Power Hazard rate distribution is introduced and its various structural properties are discussed. We examined different statistical properties like hazard rate function, quantile function, raw moment, incomplete moment and related measures, mean deviation, Bonferroni and Lorenz curve, order statistics, Renyi’s entropy, stress-strength reliability, stochastic ordering, and compounding and geometric extreme stability. The model parameter estimation is carried out using maximum likelihood estimation method. The model’s flexibility is evaluated by two real datasets by comparing it with different related models, and its application in time series is studied by the Markovian first-order autoregressive minification process.

Detectability constraints on meso-scale structure in complex networks.

Community, core-periphery, disassortative and other node partitions allow us to understand the organisation and function of large networks. In this work we study common meso-scale structures using the idea of block modularity. We find that the configuration model imposes strong restrictions on core-periphery and related structures in directed and undirected networks. We derive inequalities expressing when such structures can be detected under the configuration model which are closely related to the resolution limit. Nestedness is closely related to core-periphery and is similarly restricted to only be detectable under certain conditions. We then derive a general equivalence between optimising block modularity and maximum likelihood estimation of the parameters of the degree corrected Stochastic Block Model. This allows us to contrast the two approaches, how they formalise the structure detection problem and understand these constraints in inferential versus descriptive approaches to meso-scale structure detection.

Targeted maximum likelihood based estimation for longitudinal mediation analysis

Targeted maximum likelihood based estimation for longitudinal mediation analysis

An exploration of potential risk factors for gastroschisis using decision tree learning.

Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests. Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998-2015). Cases were gastroschisis-affected pregnancies (n=273); controls were live-born infants, frequency-matched by center (n=2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95% confidence intervals (CIs) using targeted maximum likelihood estimation. The strongest associations were observed with young maternal age (aOR 3.4, 95% CI 2.9, 4.0) and prepregnancy body-mass-index <30 kg/m2 (aOR 3.3, 95% CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education. Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.

Assessment of the Association Between Neuraxial Anesthesia and Back Pain After Delivery: A Systematic Review and Meta‐Analysis

Background: Back pain after delivery both under cesarean section and spontaneous vaginal delivery is the most common pregnancy‐related musculoskeletal problem. There are multiple studies that emphasize the effect of epidural anesthesia and spinal anesthesia on the incidence and severity of postdelivery back pain. There are others stating no association between the two.Objective: The aim of this study is to summarize the relationship between back pain after delivery and neuraxial anesthesia.Methods: Studies identified from database: Cochrane Library, The Virtual Health Library, National Library of Medicine PubMed, Google Scholar, and citation searching with both experimental and observational study design were included. Exposed and nonexposed incidence of back pain was extracted to analyze the pooled odds ratio assessing the association of postpartum back pain and neuraxial anesthesia. Heterogeneity was checked across studies using Cochrane Q test statistic and I2. Small study effect was assessed using a funnel plot graphically and nonparametric rank correlation (Begg) test.Results: Four RCT and 11 observational studies were identified for analysis. The studies included mothers delivering under cesarean section and vaginal delivery with epidural anesthesia, spinal anesthesia, and combined spinal epidural anesthesia. Based on the 15 studies included in this meta‐analysis, the pooled odds ratio according to random effect restricted maximum—likelihood model was 1.2 (95% CI (0.77–1.86)) with p value = 0.43. There was a significant heterogeneity with I2 = 97.76%, H2 = 44.58, and Cochrane Q statistics p value = 0.001.Conclusion: Our result suggests neuraxial anesthesia may not be the cause or the risk factor for the overwhelmingly high incidence of back pain women experience after delivery.

Modeling shock propagation and resilience in financial temporal networks.

Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model-a specific exponential random graph model-as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node. Unlike the standard VAR, the model is a nonlinear function of the shock size and the IRF depends on the state of the network at the shock time. We propose a novel econometric estimation method that combines the maximum likelihood estimation and Kalman filter to estimate the dynamics of the latent parameters and compute the IRF, and we apply the proposed methodology to the dynamical network describing the electronic market of interbank deposit.

Neutrosophic Burr Distribution for Modeling Health Risk Factors

The Burr distribution is one of the most important and commonly used probability distribution in statistical analysis. In this study, a new class of univariate distribution based on the Burr random variable is proposed. Characteristics of the proposed neutrosophic Burr distribution (NBD) are discussed. The neutrosophic form of the proposed distribution is particularly advantageous for handling the imprecise and uncertain information commonly present in real-world problems. The statistical properties and the shapes of corresponding probability density and cumulative density functions are illustrated. Some important functions commonly utilized in survival studies are formulated within neutrosophic structures. General expressions for other distributional properties of the proposed NBD are developed under neutrosophic framework. The inverse cumulative method is used to find random numbers from the suggested model. Maximum likelihood method for estimating the model parameters is described, and the performance of estimated parameters are assessed using a Monte Carlo simulation experiment. Finally, the paper demonstrates the practical use of the proposed model through a real-world application of malaria cases per thousand population at risk.

Phylogenetic position of the subfamily Symphrasinae (Insecta: Neuroptera), its intergeneric relationships and evolution of the raptorial condition within Mantispoidea.

The superfamily Mantispoidea (Insecta: Neuroptera) includes the families Berothidae, Rhachiberothidae and Mantispidae. Among these taxa, the last two are collectively known as Raptorial Mantispoidea due to the presence of grasping forelegs for predatory habits. The Mantispidae classically included the subfamilies Symphrasinae, Drepanicinae, Calomantispinae and Mantispinae, yet recent research challenged this classification scheme as well as the monophyly of this family resulting in Symphrasinae being transferred to Rhachiberothidae. The phylogenetic position of the subfamily Symphrasinae within Mantispoidea is here inferred based on total evidence analysis combining three genes (COI , 16S and 18S ) and 72 morphological characters scored from living representatives of all Mantispidae subfamilies (12 genera), the 3 genera of Symphrasinae, and Rhachiberothinae (1 genus). Representatives of Berothidae (four genera) and Hemerobiidae (one genus) were used as outgroup taxa. Results of the total evidence analysis were compared with parsimony and maximum likelihood analyses of the morphological and molecular datasets of the COI , 16S and 18S genes. The resultant phylogeny under total evidence recovered Rhachiberothidae as a monophyletic group with strong support in which Symphrasinae was found as sister to Rhachiberothinae. The three genera contained in Symphrasinae, i.e. Anchieta , Plega and Trichoscelia were each recovered as monophyletic in the parsimony analysis, with Anchieta as sister to Trichoscelia + Plega . The family Mantispidae was also recovered as monophyletic and sister to Rhachiberothidae, with Mantispinae as sister to Calomantispinae + Drepanicinae. Evolution of the raptorial condition in Mantispoidea is discussed based on the performed analyses. The morphology and the structure of the raptorial foreleg and the prothorax (i.e. the raptorial system) support the close relationship of Symphrasinae with Rhachiberothinae rather than to other Mantispidae subfamilies which possess a distinctive and well-differentiated raptorial apparatus. The Rhachiberothidae (including Symphrasinae) are distinguished by the presence of a foretarsal Stitz organ. Furthermore, a sit-and-wait predatory strategy is hypothesised for this taxon, whereas the Mantispidae are likely sophisticated active-ambushing predators.

Neutrosophic Topp-Leone Extended Exponential distribution modeling with application for bladder cancer patients

The Topp-Leone Extended Exponential distribution is used to simulate human lifetime data patterns in the field of survival analysis. To characterize a variety of uncertain survival data, the neutrosophic Topp-Leone extended exponential distribution (NTLEED) is used. The specified distribution is a great tool for modeling unknown data that is somewhat positively biased. This study covers the primary statistical properties of the constructed NTLEED, including the survival function, hazard rate, and neutrosophic moments. In addition, the neutrosophic parameters are estimated using the popular maximum likelihood estimation technique. To determine whether the predicted neutrosophic parameters were obtained, a simulation study is carried out. Not to mention that actual data has been used to discuss potential real-world applications of NTLEED. Real data were utilized to demonstrate how well the proposed model performed in contrast to the current distributions.

Patterns of drug combinations used by men who have sex with men in Brazil.

The practice of using drugs to modulate experiences is a well-documented phenomenon worldwide, particularly among men who have sex with men (MSM). This study aims to describe patterns of drug combinations used by Brazilian MSM, embracing a detailed examination of the Brazilian context, which may inspire research elsewhere while enriching the global panorama on MSM drug use. By focusing on Brazilian MSM, the study sheds light on specific drug use combinations, which may help in the development of effective, culturally sensitive public health strategies. This investigation was conducted within the Latin American MSM Internet Survey (LAMIS), a pioneer online study addressing psycho-social and sexual health of MSM in the region. Conducted from January to May 2018, the survey garnered responses from over 18,139 Brazilian MSM. Using exploratory factor analysis through maximum likelihood estimation, the study identifies distinct sub-groups of drug users among 15,499 participants after exclusion due to the presence of discrepant responses. Kaiser-Meyer-Olkin measure (KMO) = 0.94) and Bartlett's Test = < 0.01) indicating adequate sampling and suitability for factor analysis across all substances. The factor analysis revealed four primary drug combination groups among participants: Group One (Heavy Drugs Users), Group Two (Party Drug Users), Group Three (Conservative Users), and Group Four (Classic Chemsex Users). Each group represents unique drug use patterns and preferences, from the use of potent stimulants to more socially accepted substances like tobacco and cannabis. Notably, Group One, identified as "heavy" drugs users, engaged in the use of particularly risky substances, including mephedrone, crack, crystal meth, heroin and other synthetic stimulants. The findings reveal diverse and complex drug use patterns among MSM in Brazil. Understanding these patterns is essential for developing targeted interventions and support mechanisms for various sub-groups within MSM. Future research should focus on the health outcomes associated with these patterns and the social and psychological contexts of drug use.

Vaginal antiseptic preparation at the time of hysterectomy: a systematic review and meta-analysis.

This study aimed to compare the rates of surgical site infection after hysterectomy using vaginal antisepsis with chlorhexidine gluconate vs povidone-iodine. PubMed, Embase, and ClinicalTrials.gov databases were queried from January 1, 1985 through December 7,2023. Randomized controlled trials and nonrandomized comparisons of interventions of chlorhexidine gluconate and povidone-iodine vaginal antiseptic preparation before hysterectomy were included. The primary outcome was surgical site infection as defined by the Centers for Disease Control and Prevention. Secondary outcomes included postoperative urinary tract infections and vaginal culture specimens. Large single-group studies of vaginal chlorhexidine gluconate reporting adverse events and case reports of desquamation were also included. The methodologic quality of each study was assessed using the original Cochrane RoB (Risk of Bias) and ROBINS-I (Risk of Bias in Non-Randomized Studies - of Interventions) tools. Restricted maximum likelihood meta-analyses of odds ratios were conducted. A total of 5289 abstracts were screened; 10 met the inclusion criteria, with a total of 9618 participants. The studies included 4 randomized controlled trials (n=306), 3 prospective nonrandomized comparisons of interventions (n=1089), and 3 retrospective nonrandomized comparisons of interventions (n=8223). Povidone-iodine was compared with 4% chlorhexidine gluconate in 4 studies (n=2491), 2% chlorhexidine gluconate in 2 studies (n=236), 0.1% chlorhexidine gluconate in 1 study (n=50), and both 2% and 4% chlorhexidine gluconate in 1 study (n=49). Meta-analysis revealed no statistically significant difference in surgical site infections, although they were somewhat more common with chlorhexidine gluconate (summary odds ratio, 1.22; 95% confidence interval, 0.91-1.63). The relative effect of antiseptic preparations on urinary tract infections was unclear, with an imprecise summary odds ratio (1.18; 95% confidence interval, 0.65-2.12). Positive vaginal cultures were less common with chlorhexidine gluconate preparation (summary odds ratio, 0.10; 95% confidence interval, 0.04-0.27). Two studies reported no adverse events with chlorhexidine gluconate, and 2 found no difference in vaginal irritation when compared with povidone-iodine. Two case reports described vaginal desquamation or hypersensitivity with chlorhexidine gluconate. There is no evidence of a difference in postoperative infection with chlorhexidine gluconate use compared with povidone-iodine, but chlorhexidine gluconate vaginal preparation results in lower rates of positive intraoperative vaginal cultures. Despite inadequate reporting, risk of adverse events appears low.

Towards Maximum Likelihood Training for Transducer-Based Streaming Speech Recognition

Towards Maximum Likelihood Training for Transducer-Based Streaming Speech Recognition

Modeling bladder cancer survival function based on neutrosophic inverse Gompertz distribution

In the field of survival analysis, the inverse Gompertz distribution is used to mimic human lifetime data patterns. The goal of the neutrosophic inverse Gompertz distribution (NIGD) is to describe a range of indeterminate survival data. The defined distribution is very helpful for modeling somewhat positively skewed unknown data. The main statistical characteristics of the created NIGD, such as the neutrosophic moments, hazard rate, and survival function, are covered in this paper. Additionally, the well-known maximum likelihood estimation method is used to estimate the neutrosophic parameters. A simulation study is conducted to see whether the projected neutrosophic parameters were reached. Not to mention that possible real-world uses of NIGD have been discussed using actual data. To show how well the suggested model performed in comparison to the present distributions, real data were used.

Probing jet dynamics and collimation in radio galaxies

Context. Radio galaxies with visible two-sided jet structures, such as NGC 1052, are sources of particular interest for studying the collimation and shock structure of active galactic nuclei jets. High-resolution very long baseline interferometry observations of such sources can resolve and study the jet collimation profile and probe different physical mechanisms. Aims. In this paper, we study the physics of double-sided radio sources at parsec scales, and in particular investigate whether propagating shocks can give rise to the observed asymmetry between a jet and a counterjet. Methods. We carried out special relativistic hydrodynamic simulations and performed radiative transfer calculations of an over-pressured perturbed jet. During the radiative transfer calculations, we incorporated both thermal and nonthermal emission, while taking the finite speed of light into account. To further compare our results to observations, we created more realistic synthetic data including the properties of the observing array as well as the image reconstruction via multifrequency regularized maximum likelihood methods. We finally introduced a semiautomated method of tracking jet components and extracting jet kinematics. Results. We show that propagating shocks in an inherently symmetric double-sided jet can lead to partially asymmetric jet collimation profiles due to time delay effects and relativistic beaming. These asymmetries may appear in specific epochs, with one jet evolving near conically and the other one parabolically (the width profile evolving with a slope of ≈1 and ≈0.5, respectively). However, these spurious asymmetries are not significant when observing the source evolve for an extended amount of time. Conclusions. Purely observational effects are not enough to explain a persistent asymmetry in the jet collimation profile of double-sided jet sources and hint at evidence for asymmetrically launched jets.