Parameters Of Regression Model Research Articles

#1253 Predicting renal outcomes in ANCA-associated vasculitis—validation of ANCA Renal Risk Score in a Portuguese cohort

Abstract Background and Aims Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare cause of glomerulonephritis and clinical presentation can be diverse. Despite recent developments in immunosuppressive treatments, AAV with renal involvement is still associated with poor renal prognosis and a high risk of end-stage renal disease (ESRD). Kidney biopsy confirms the diagnosis, details the degree of active inflammation and chronicity of disease, also informing of renal prognosis. Berden histological classification categorizes renal histological findings into four classes but the ability to accurately predict renal outcomes varies among studied cohorts. In 2018 Brix et al developed and validated a new risk score, ANCA Renal Risk Score (ARRS), to determine renal outcomes based on histopathological and clinical indicators, with accurate ESRD prediction at 36 months. This study aims to assess the predictive value of ARRS in renal survival on a Portuguese cohort with newly diagnosed AAV with kidney involvement. Method We designed a retrospective study examining ANCA vasculitis in native kidney biopsies evaluated in a Portuguese kidney histomorphology-dedicated center between 2004 and 2023. Data collected from biopsies included percentage of normal glomeruli (PNG) and interstitial fibrosis/tubular atrophy (IFTA). Clinical and demographical data was also collected, including age, sex, estimated glomerular filtration rate (eGFR) at presentation, date of diagnosis, ESRD and date of death. We calculated ARRS based on eGFR, PNG and IFTA, and patients were classified as being at low, intermediate and high risk of developing ESRD by 36 months. Primary endpoint was established as difference in kidney survival at 36 months. Secondary endpoint included difference in overall survival at 36 months and the contribution of each of the ARRS parameters in a Cox regression model. Results A total of 143 patients were obtained, 46.2% female, with a mean age at diagnosis of 67.1 years. There was a predominancy of MPO-related disease (77.2%) and 69% presented with rapidly progressive renal insufficiency. Median ARRS was 6.5, with 10.8%, 44.3% and 44.9% classified as low, intermediate, and high risk, respectively. Kaplan Meier estimates for renal survival computed at 36 months revealed an overall mean time to dialysis of 20 months, which decreased with an increase in risk category [33.4 (low risk), 24.7 (intermediate risk), 12.7 months (high risk), Log-R test: χ2(2) = 24.9, p < 0.001] with an ESRD survival of 92.9%, 63.6% and 29.7% at 36 months, respectively. Regarding secondary endpoints, overall survival at 36 months was not different between groups [33.7 (low risk) vs. 33.2 (intermediate risk) vs. 32.2 months (high risk), Log-R test: χ2(2) = 0.3, p = 0.9]. Cox regression, plotted for ESRD at 36 months using the three score variables, revealed eGFR at presentation as the strongest predictor of kidney survival (HR: 0.96, 95% CI: 0.93-0.99, p = 0.004) whereas PNG and IFTA did not achieve significance (p = 0.189 and p = 0.066, respectively). Conclusion Our results corroborate Brix et al ARRS, accurately predicting ESRD in a Portuguese cohort of patients with renal involvement by ANCA vasculitis. Patients assessed as high-risk of developing ESRD had a lower mean time to dialysis, although overall survival did not differ between groups. Contrary to Brix et al findings, which revealed PGN as the only independent predictor of ESRD, in our study lower eGFR at the time of diagnosis was an independent risk factor. Type of immunosuppression used in each patient was not considered in this study and could maybe alter results of renal and overall survival. Despite this, Brix et al reported that, in their population, validity of ARRS was not influenced by immunosuppressive treatment. This new scoring system still requires validation in a larger cohort, but results seem promising and, in the future, may aid clinicians with treatment decisions. This is relevant since full immunosuppression induction schemes may be halted in patients with high probability of ESRD, diminishing infectious complications and potentially decreasing morbidity and mortality associated with AAV.

External vs Internal Focus of Attention in Procedural Skills Learning: A Randomized Study.

The Optimizing Performance Through Intrinsic Motivation and Attention for Learning theory postulates that directing attention to the intended movement effect or outcome (external focus) is more effective than directing attention to the internal body motion or body part (internal focus). This study compared external vs internal focus of attention as novice students learned ultrasound-guided peripheral cannulation. The authors hypothesized that conditions promoting an external (vs internal) focus of attention would result in better performance on immediate and delayed (retention) testing. The authors conducted a randomized, experimental study from October 2022 to February 2023 comparing external and internal focus using ultrasound-guided peripheral venous cannulation followed by practice using an ultrasound training block. Undergraduate health science students performed 4 practice trials with instructions to focus on the needle tip (external focus) or their hands (internal focus), followed by an immediate assessment and a retention assessment 2 weeks later. The primary outcome was time to successful cannulation. Results were compared using Kaplan-Meier curves and parametric survival analysis regression (accelerated failure time) models. Seventy participants completed the immediate assessment. The external focus group completed successful cannulation a mean of 2.33 times faster (95% CI = 1.47, 3.67; P < .001) than those in the internal focus group (median time, 12.0 vs 29.5 seconds). Results also favored the external focus group during practice, with the external focus group successfully completing the task a mean of 1.83 times faster (95% CI = 1.04, 3.21; P = .04) than the internal focus group. In the retention assessment, the external focus group successfully completed the task a mean of 2.37 times faster (95% CI = 1.19, 4.74; P = .02) than the internal focus group. External focus of attention compared with internal focus of attention improves medical motor skill learning in novice learners.

Statistical analysis on the incidence and predictors of death among second-line ART patients in public hospitals of North Wollo and Waghemira Zones, Ethiopia, 2021

Acquired immune deficiency virus, caused by the human immunodeficiency virus, is a significant global health concern. Sub-Saharan Africa particularly Ethiopia faces a high prevalence of human immunodeficiency virus. In low-income settings like Ethiopia, early mortality rates are elevated due to severe opportunistic infections and advanced disease at Anti-retroviral treatment initiation. Despite available treatments, delayed treatment initiation among Human Immunodeficiency Virus -infected individuals in Africa, including Ethiopia, leads to disease progression and increased mortality risk. This study aimed to identify the factors contributing to the death of HIV patients under treatment at second line regimen in public hospitals of North Wollo and Waghemira Zones. A retrospective cohort study with 474 patients was conducted in selected hospitals of North Wollo and Waghemira Zones. A parametric Weibull regression model was employed, and the adjusted hazard ratio served as the measure of association. Variables significantly affected the outcome of the study was determined at a p-value < 0.05, along with a 95% confidence interval for the variables. The patients were within the average age of 38.6(standard deviation ± 12.5) years and majority (45.57%) had no formal education. The overall death incidence rate among second-line anti-retroviral treatment patients was 1.98 per 100-person years [95% CI 1.4—2.9%]. Poor adherence to antiretroviral treatment, male gender, and being underweight significantly increased the hazard of death. Conversely, increased anti-retroviral treatment duration had a significant and negative impact, reducing the hazard of death among patients. The study reveals a high incidence of death among second line anti-retroviral treatment users. Independent predictors include poor adherence, male gender, and underweight status, all significantly increasing the risk of death. On the positive side, the hazard of death decreases with longer anti-retroviral treatment duration. A critical concern and counseling should be given for better ART adherence, to change their nutritional status and for males.

Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity

In Japan, the stability of cut slopes alongside expressways has deteriorated over time due to factors such as weathering, rendering them susceptible to significant collapses, especially during heavy rainfall. This study presents an evaluation model to assess the stability of cut slopes, with the aim of capturing their long-term deterioration characteristics using the logistic regression method. Additionally, to explore variations in deterioration patterns among cut slopes in diverse regions with varying geological compositions, a hierarchical Bayesian model is utilized to estimate parameters in the logistic regression model. This methodology enables the creation of a novel stability probability evaluation model for cut slopes that is adaptable to regions with different geological structures. Moreover, to comprehensively evaluate changes in the disaster resistance capabilities of extensively used cut slopes, the post-disaster recovery capabilities in the slope region are assessed through a comprehensive weighted evaluation method (EWM-TOPSIS). Building on this assessment, a novel, simplified, and more generalized resilience evaluation method is proposed. By integrating stability probability and resilience, an evaluation metric named "disaster immunity" is introduced to measure alterations in the disaster risk resistance of extensively utilized cut slopes. A case study is presented to illustrate the application of the evaluation model on selected cut slopes from various regions. This evaluation framework can assist road managers in predicting the disaster risk status of extensively used cut slopes and identifying priority slopes for implementing preventive measures to mitigate potential losses in the event of slope disasters.

Changes in hepatitis C virus prevalence and incidence among people who inject drugs in the direct acting antiviral era

BackgroundThe World Health Organization (WHO) has set a goal to eliminate hepatitis C virus (HCV) infection by 2030, including a 90% reduction of HCV incidence. With the introduction of a needle syringe program (NSP) in Stockholm, Sweden, and unrestricted availability of direct acting antiviral (DAA) treatment, we investigate the change of prevalence and incidence of HCV infection among people who inject drugs (PWID) over time. MethodsAll persons attending the Stockholm NSP 2013-2021 (n=4,138) were included. The prevalence of viremic HCV infection was investigated yearly. For incidence analysis, PWID at risk with at least one follow-up test were included. Participants were divided into naive defined as anti-HCV negative (n=791), and exposed, defined as anti-HCV positive with a negative HCV RNA (n=1,030). Risk factors for HCV infection were analyzed using parametric exponential proportional hazards regression models. ResultsThe prevalence of viremic HCV infection decreased from 62% to 30% year 2013-2021 while the prevalence of cured after treatment increased from 0 to 22%, corresponding to 42% cured after treatment out of eligible in 2021. The overall incidence rate in naive was 16.9 (95% CI 15.0-19.0) and in exposed 12.8 (95% CI 11.6-14.2) per 100 person years (PY) and was not significantly reduced years 2013-2015 to 2020-2021 in either group. Risk factors for incident HCV infection in multivariable analysis were sharing needles/syringes, younger age, custody/prison past year, and homelessness, whereas opioid agonist treatment was protective. ConclusionThe prevalence of HCV was halved in PWID as unrestricted DAA treatment became available and NSP was established in Stockholm. However, overall incidence was not reduced. To meet the WHO incidence goal, targeting PWID with high injection risk behaviors for testing and treatment is essential, along with engagement in harm reduction services.

Circulating HMGB1 in acute ischemic stroke and its association with post-stroke cognitive impairment.

Ischemic stroke frequently leads to a condition known as post-stroke cognitive impairment (PSCI). Timely recognition of individuals susceptible to developing PSCI could facilitate the implementation of personalized strategies to mitigate cognitive deterioration. High mobility group box 1 (HMGB1) is a protein released by ischemic neurons and implicated in inflammation after stroke. Circulating levels of HMGB1 could potentially serve as a prognostic indicator for the onset of cognitive impairment following ischemic stroke. To investigate the predictive value of circulating HMGB1 concentrations in the acute phase of ischemic stroke for the development of cognitive dysfunction at the 3-month follow-up. A total of 192 individuals experiencing their initial episode of acute cerebral infarction were prospectively recruited for this longitudinal investigation. Concentrations of circulating HMGB1 were quantified using an enzyme-linked immunosorbent assay (ELISA) technique within the first 24 hours following hospital admission. Patients underwent neurological evaluation including NIHSS scoring. Neuropsychological evaluation was conducted at the 3-month follow-up after the cerebrovascular event, employing the Montreal Cognitive Assessment (MoCA) as the primary tool for assessing cognitive performance. Multivariable logistic regression models were employed to investigate the relationship between circulating HMGB1 concentrations and cognitive dysfunction following stroke, which was operationalized as a MoCA score below 26, while controlling for potential confounders including demographic characteristics, stroke severity, vascular risk factors, and laboratory parameters. Of 192 patients, 84 (44%) developed PSCI. Circulating HMGB1 concentrations were significantly elevated in individuals who developed cognitive dysfunction following stroke compared to those who maintained cognitive integrity (8.4 ±1.2 ng/mL vs 4.6 ±0.5 ng/mL, respectively; p<0.001). The prevalence of PSCI showed a dose-dependent increase with higher HMGB1 quartiles. After controlling for potential confounders such as demographic factors (age, gender, and education), stroke severity, vascular risk factors, and laboratory parameters in a multivariable logistic regression model, circulating HMGB1 concentrations emerged as a significant independent predictor of cognitive dysfunction following stroke (regression coefficient = 0.236, p<0.001). Circulating HMGB1 concentrations quantified within the first 24 hours following acute cerebral infarction are significantly and independently correlated with the likelihood of developing cognitive dysfunction at the 3-month follow-up, even after accounting for potential confounding factors. HMGB1 may be a novel biomarker to identify patients likely to develop post-stroke cognitive impairment for targeted preventive interventions.

A learning system-based soft multiple linear regression model

Machine learning applied to regression models offers powerful mathematical tools for predicting responses based on one or more predictor variables. This paper extends the concept of multiple linear regression by implementing a learning system and incorporating both fuzzy predictors and fuzzy responses. To estimate the unknown parameters of this soft regression model, the approach involves minimizing the absolute distance between two lines under three constraints related to the absolute error distance between observed data and their respective predicted lines. A thorough comparative analysis is conducted, showcasing the practical applicability and superiority of the proposed soft multiple linear regression model. The effectiveness of the model is demonstrated through a comprehensive examination involving simulation studies and real-life application examples.

A new class of composite GBII regression models with varying threshold for modeling heavy-tailed data

The four-parameter generalized beta distribution of the second kind (GBII) has been proposed for modeling insurance losses with heavy-tailed features. The aim of this paper is to present a parametric composite GBII regression modeling by splicing two GBII distributions using mode matching method. It is designed for simultaneous modeling of small and large claims and capturing the policyholder heterogeneity by introducing the covariates into the scale parameter. The threshold that splits two GBII distributions is allowed to vary across individuals policyholders based on their risk features. The proposed regression modeling also contains a wide range of insurance loss distributions as the head and the tail respectively and provides the close-formed expressions for parameter estimation and model prediction. A simulation study is conducted to show the accuracy of the proposed estimation method and the flexibility of the regressions. Some illustrations of the applicability of the new class of distributions and regressions are provided with a Danish fire losses data set and a Chinese medical insurance claims data set, comparing with the results of competing models from the literature.

Association of Androgen Hormones, Sex Hormone-Binding Globulin, and the Menopausal Transition With Incident Diabetes Mellitus in Women With and Without HIV.

HIV is associated with alterations in androgen hormone levels and sex hormone-binding globulin (SHBG) in women. Higher SHBG has been associated with a lower risk of diabetes in the general population, but the contribution of HIV, androgen hormones, SHBG, and menopausal phase to diabetes is unclear. From April 2003 through February 2020, 896 women with HIV (WWH) and 343 women without HIV (WWOH) from the Women's Interagency HIV Study with morning total testosterone, dehydroepiandrosterone sulfate (DHEAS), and SHBG levels were followed to assess for incident diabetes. Parametric regression models were used with age as the time scale and relative times (RT) as the measure of association of hormone level and menopausal phase with incident diabetes. Analyses incorporated time-dependent androgen hormone, SHBG levels, and menopausal phase and were adjusted for race/ethnicity, enrollment year, smoking status, BMI, hepatitis C virus status, and HIV-related factors. In total, 128 (14%) WWH and 47 (14%) WWOH developed diabetes. In WWH, a doubling of SHBG and DHEAS were associated with a 7% (RT = 1.07 [95% CI: 0.82 to 1.40] and 15% (RT = 1.15 [95% CI: 0.95 to 1.39]) longer time to diabetes, respectively; in WWOH, a doubling of SHBG and DHEAS were associated with 84% (RT = 1.84 [95% CI: 0.89 to 3.82]) and 41% (RT= 1.41 [95% CI: 0.82 to 2.44]) longer times to diabetes. Total testosterone was not associated. In WWH, later menopausal phase was associated with shorter times to diabetes. Despite alterations in androgen hormone and SHBG levels in HIV, regardless of HIV status, higher SHBG and DHEAS were associated with nonstatistically significant slower progression to diabetes. The menopausal transition may be a better hormonal indicator of diabetes risk in WWH.

Marijuana Use Increases Risk of Hematoma Formation in Patients Undergoing Abdominal Body Contouring Surgery

Abstract Goals/Purpose The physiological effect of marijuana is thought to include platelet inhibition and poor wound healing, however, there is mixed clinical evidence in the plastic surgery literature regarding this topic. As marijuana usage continues to increase, this study sought to identify the effect of marijuana on postoperative complication rates in patients undergoing abdominal body contouring surgery (ABCS) while controlling for tobacco consumption and other comorbidities. Methods/Technique A retrospective cohort study was conducted in patients who underwent panniculectomy or abdominoplasty at our institution between 2016 and 2021. Patients were separated into groups of active (at time of surgery), former, and no marijuana use. Demographic characteristics, smoking history, laboratory results, comorbidities, operative details, and postoperative complications including hematoma, deep vein thrombosis (DVT) and pulmonary embolism (PE), and wound healing complications were analyzed. Parametric, nonparametric, and multivariable regression modeling was used for analysis. Results/Complications 815 patients who underwent panniculectomy or abdominoplasty were included in the study. 61 patients (7.5%) reported active marijuana use at time of their surgery, and 31 patients (3.8%) reported former marijuana use (defined as last use &amp;gt;2 months prior). Patients who reported any marijuana use were significantly younger (40.5 years vs 45.9 years; p&amp;lt;0.0001) and more likely to identify as Black (OR=2.34; p=0.005). Any marijuana use was significantly associated with reported tobacco use (OR=4.80; p&amp;lt;0.001; 95% CI 1.69-12.69) After adjusting for age, BMI, tobacco use, platelet count, and comorbidity index, active marijuana use was associated with significantly higher risk of hematoma formation (OR=2.55; P= 0.03; 95% CI 1.12-6.55) as well as any complication combined (OR=1.73; p=0.02, 95% CI 1.15-3.56). Other complications, including venous thromboembolism, infection, seroma, umbilical necrosis, wound dehiscence, or anesthetic-related complications were not significantly associated with reported marijuana use. Conclusion When controlling for multiple confounders, marijuana use appears to be independently associated with increased risk of hematoma development in patients undergoing abdominal body contouring surgery with either abdominoplasty or panniculectomy. Further research is warranted to investigate the exact relationships and mechanisms of action behind this finding.

Iterative‐recursive estimation of parameters of regression models with resistance to outliers on practical examples

AbstractHere, identification of processes and systems in the sense of the least sum of absolute values is taken into consideration. The respective absolute value estimators are recognised as exceptionally insensitive to large measurement faults or other defects in the processed data, whereas the classical least squares procedure appears to be completely impractical for processing the data contaminated with such parasitic distortions. Since the absolute value quality index cannot be minimised analytically, an iterative solution is used to find optimal estimates of the parameters of the underlying regression model. In addition, an approximate recursive estimator is proposed and implemented for on‐line evaluation of system parameters. The convergence (basic property) of the iterative estimator is show to be proven and some aspects related to the absolute value criterion are explained. This allows for the formulation of practical conclusions and indication of directions for further research. In addition, the effectiveness of the described iterative‐recursive estimation procedures is practically verified by appropriate numerical experiments.

Locally time-varying parameter regression

. I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters to mix over different frequencies of parameter changes in a data driven way and permits great flexibility while achieving model parsimony. A convenient strategy is discussed to infer if each coefficient is static or dynamic and, if dynamic, how frequent the parameter change is. An MCMC scheme is developed for model estimation. The performance of the proposed approach is illustrated in studies of both simulated and real economic data.

Patterns of Change in Nucleotide Diversity Over Gene Length.

Nucleotide diversity at a site is influenced by the relative strengths of neutral and selective population genetic processes. Therefore, attempts to estimate Effective population size based on the diversity of synonymous sites demand a better understanding of their selective constraints. The nucleotide diversity of a gene was previously found to correlate with its length. In this work, I measure nucleotide diversity at synonymous sites and uncover a pattern of low diversity towards the translation initiation site of a gene. The degree of reduction in diversity at the translation initiation site and the length of this region of reduced diversity can be quantified as "Effect Size" and "Effect Length" respectively, using parameters of an asymptotic regression model. Estimates of Effect Length across bacteria covaried with recombination rates as well as with a multitude of translation-associated traits such as the avoidance of mRNA secondary structure around translation initiation site, the number of rRNAs, and relative codon usage of ribosomal genes. Evolutionary simulations under purifying selection reproduce the observed patterns and diversity-length correlation and highlight that selective constraints on the 5'-region of a gene may be more extensive than previously believed. These results have implications for the estimation of effective population size, and relative mutation rates, and for genome scans of genes under positive selection based on "silent-site" diversity.

Effects of esketamine nasal spray on depressive symptom severity in adults with treatment-resistant depression and associations between the Montgomery-Åsberg Depression Rating Scale and the 9-item Patient Health Questionnaire.

To examine the effect of esketamine nasal spray (ESK) plus newly initiated oral antidepressant (OAD) versus OAD plus placebo nasal spray (PBO) on the association between Montgomery-Åsberg Depression Rating Scale (MADRS) and 9-item Patient Health Questionnaire (PHQ-9) scores in adults with treatment-resistant depression (TRD). Data from TRANSFORM-1 and TRANSFORM-2 (two similarly designed, randomized, active-controlled TRD studies) and SUSTAIN-1 (relapse prevention study) were analyzed. Group differences for mean changes in PHQ-9 total score from baseline were compared using analysis of covariance. Associations between MADRS and PHQ-9 total scores from TRANSFORM-1/TRANSFORM-2 were assessed using simple parametric, nonparametric, and multiple regression models. In TRANSFORM-1/TRANSFORM-2 (ESK+OAD, n=343; OAD+PBO, n=222), baseline PHQ-9 mean scores were 20.4 for ESK+OAD and 20.6 for OAD+PBO (severe depression). At day 28, significant group differences were observed in least squares mean change (SE) in PHQ-9 scores from baseline (-12.8 [0.46] vs -10.3 [0.53], P<.001) and in clinically substantial change in PHQ-9 scores (≥6 points; 77.1% vs 64%, P<.001) in ESK+OAD and OAD+PBO groups, respectively. A nonlinear relationship between MADRS and PHQ-9 was observed; total scores demonstrated increased correlation over time. In SUSTAIN-1, 57.3% of patients receiving ESK+OAD (n=89) versus 44.2% receiving OAD+PBO (n=86) retained remission status (PHQ-9 score ≤4) at maintenance treatment end point (P=.044). In adults with TRD, ESK+OAD significantly improved severity of depressive symptoms, and more patients achieved clinically meaningful changes in depressive symptoms based on PHQ-9, versus OAD+PBO. PHQ-9 outcomes were consistent with those of clinician-rated MADRS. ClinicalTrials.gov: NCT02417064, NCT02418585, NCT02493868.

Building energy efficiency assessment base on predict-center criterion under diversified conditions

With the rapid expanding of urban economy and population, how to reduce the energy consumption and improve energy efficiency of public buildings has become a key issue for energy conservation and urban development. However, due to the diversity of building usage condition, energy consumption data often has problems with nonlinearity, outliers, and diversity, which greatly reduce the accuracy of energy consumption prediction and energy conservation assessment. To overcome the above drawbacks, a regression prediction model of building energy consumption is designed, which cluster the same type of data characteristics to form a subset of equipment through Gaussian mixture clustering, and design the specific normalized adaptive loss function to determine the optimal control parameters of regression model. Furthermore, a novel predict-center energy efficiency evaluation criterion is proposed, and the cumulative distribution pattern is further statistically analyzed. Finally, the energy benchmark evaluation framework is proposed to determine the energy efficiency evaluation grade. Through comparative experimental analysis, it can be seen that the proposed prediction model can adapt to different types of building data, and have better prediction accuracy and convergence speed. The consistency and robustness of the benchmark evaluation framework and method are optimal.

Estimation Parameters in the fuzzy class Poisson mixture regression model

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

AbstractMoisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non‐destructively, hyperspectral imaging technology (400‐1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG‐SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R2p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content.

A comparison between the estimation of the exponential and Weibull survival regression models using the Bayesian method (Simulation)

Parametric regression models are one of the most important regression models used in the medical field. They are the tool through which the response variable is modeled when the values ​​of that variable are survival times with a known probability distribution such as the exponential distribution and the Weibull distribution, I propose in this article to estimate the parameters of parametric survival regression models using the Bayesian method, these models are: the exponential survival regression model, the Weibull survival regression model, The simulation was used to generate data that follows the parametric survival regression models depending on various factors such as sample size, and three different parameter models, The simulation results showed that the exponential survival regression model outperformed the Weibull survival regression model in order to obtain the lowest value according to the Bayesian Information Criterion (BIC), The larger the sample size, the more accurate and reliable the analysis estimators performed by the Bayes Information Criterion

Health and growth in China: Greater investment in both soft- and hard-ware are needed

The purpose is to study the relationship between health investment and economic growth in China. The research design involves empirical research using semi parametric regression models and instrumental variable methods. The results indicate that on the one hand, Chinese health investment can promote economic growth, and its promoting effect on economic growth is non-linear; On the other hand, healthcare investment in the form of human capital is more conducive to promoting regional economic growth than healthcare investment in the form of material capital. The conclusion is that it is necessary to increase health investment in the form of human capital such as the number of health personnel in the eastern region, as well as in the form of material capital such as the number of beds in the central and western regions, in order to better promote regional economic growth.

Complete convergence for arrays of rowwise m n -extended negatively dependent random variables and its application

. In this article, under some proper and sufficient conditions, we gave complete convergence for weighted sums and maximal weighted sums of arrays of rowwise m n -extended negatively dependent (rowwise m n -END) random variables, which is a new dependent structure. In addition, a relationship between { m n , n ≥ 1 } and moment condition for convergence is revealed in a sense. The results obtained in the article generalize some corresponding ones for independent and some dependent random variables. As an application, the strong consistency for the weighted estimator in a non parametric regression model is established.