Estimating Regression Models Research Articles

Disability, childhood experiences of violence and associated health outcomes in refugee settlements in Uganda

BackgroundThere is limited evidence regarding the associations between disability, childhood experiences of violence, and associated health outcomes in humanitarian settings. ObjectiveWe examined the prevalence of childhood sexual, physical, and emotional violence by disability status, explored associations between childhood violence and type of disability (limitation), perpetrator types, and the negative health outcomes associated with experiencing childhood violence by disability status. Participant and SettingParticipants included 1338 females and 927 males aged 13–24 years living in refugee settings in Uganda. MethodsData were from a cross-sectional Humanitarian Violence against Children and Youth Survey (HVACS) conducted between March and April 2022 in Uganda. Analysis entailed cross-tabulation with a chi-square test and estimation of bivariate and multivariate logistic regression models. ResultsFor both females and males, the prevalence of sexual violence in childhood was higher among those with disabilities compared to those without disabilities (23.2% vs. 11.5% for females; and 15.7% vs. 7.6% for males). The odds of experiencing sexual violence were higher among females with physical limitations (self-care [AOR:2.1; 95%CI-1.0-4.3] and task performance [AOR:2.5; 95%CI = 1.3–5.2]) and males with both physical [AOR:4.4; 95%CI = 1.4–13.7] and communication [AOR:4.1; 95%CI = 1.3–12.9] limitations compared to those without such limitations. Experiencing violence and having disabilities increased the odds of reporting negative health outcomes including severe mental distress and symptoms or being diagnosed with STI among females by three times. ConclusionIn Uganda's refugee settings, the prevalence of childhood violence is higher among children and youth with disabilities compared to those without disabilities. Females with disabilities and who had experienced childhood violence were considerably more susceptible to negative health outcomes. These findings underscore the need for targeted child protection and response interventions to address the vulnerabilities of children and youth, and particularly for those with disabilities and female children.

THE IMPACT OF THE INCREASE IN THE VOLUME OF FREIGHT TURNOVER ON THE RAILWAYS ON EXPORTS

Purpose: This study aims to investigate the intricate relationship between the volume of exports (Y) and freight turnover on railways (X) through a paired linear regression model. The purpose is to discern the quantitative impact of railway freight turnover on a nation's export volumes and offer insights for policymakers and stakeholders in the transportation and trade sectors. Design/Methodology/Approach: A quantitative research design is employed, utilizing historical data spanning from 2000 to 2022. The chosen methodology involves the estimation of a paired linear regression model using the method of least squares. Statistical significance is tested through the coefficient of determination, Fisher's F-test, and an examination of heteroskedasticity. Elasticity analysis, rank correlation, and graphical assessments of residuals provide a comprehensive understanding of the relationship. Findings: The research reveals a strong and statistically significant positive correlation (r = 0.92) between export volumes and railway freight turnover. The regression model, validated through multiple tests, explains 84.72% of the variability in export volumes. Economic interpretation indicates that a one-unit increase in railway freight turnover leads to a substantial average increase of 1627720.728 units in export volumes. The absence of heteroskedasticity reinforces the robustness of the model.

Improving the daily pan evaporation estimation of long short-term memory and support vector regression models by using the Wild Horse Optimizer algorithm

ABSTRACT Evaporation is a basic element in the hydrological cycle that plays a vital role in a region's water balance. In this paper, the Wild Horse Optimizer (WHO) algorithm was used to optimize long short-term memory (LSTM) and support vector regression (SVR) to estimate daily pan evaporation (Ep). Primary meteorological variables including minimum temperature (Tmin), maximum temperature (Tmax), sunshine hours (SSH), relative humidity (RH), and wind speed (WS) were collected from two synoptic meteorological stations with different climates which are situated in Fars province, Iran. One of the stations is located in Larestan city with a hot desert climate and the other is in Abadeh city with a cold dry climate. The partial mutual information (PMI) algorithm was utilized to identify the efficient input variables (EIVs) on Ep. The results of the PMI algorithm proved that the Tmax, Tmin, and RH for Larestan station and also the Tmax, Tmin, and SSH for Abadeh station are the EIVs on Ep. The results showed the LSTM–WHO hybrid model for both stations can ameliorate the daily Ep estimation and it can also reduce the estimation error. Therefore, the LSTM–WHO hybrid model was proposed as a powerful model compared to standalone models in estimating daily Ep.

Adult use cannabis legalization and cannabis use disorder treatment in California, 2010–2021

IntroductionMany nations and jurisdictions have legalized non-medical adult use of cannabis, or are considering doing so. This paper contributes to knowledge of adult use legalization's associations with cannabis use disorder (CUD) treatment utilization. MethodsThis study collected data from a dataset of all publicly funded substance use disorder treatment delivered in California from 2010 to 2021 (1,460,066 episodes). A logistic regression model estimates adult use legalization's impacts on CUD treatment utilization using an individual-level pre-post time series model, including individual and county-level characteristics and county and year-fixed effects. ResultsAdult use legalization was associated with a significant decrease in the probability of admission to CUD treatment (average marginal effect (AME): −0.005, 95 % CI: −0.009, 0.000). Adult use legalization was also associated with a decrease in the probability of admission to CUD treatment for males (AME: −0.025, 95 % CI: −0.027, −0.023) Medi-Cal beneficiaries (AME: −0.025, 95 % CI: −0.027, −0.023) adults ages 21+ (AME: −0.011, 95 % CI: −0.014, −0.009) and Whites (AME: −0.012, 95 % CI: −0.015, −0.010), and an increase in the probability of admission to CUD treatment for patients referred from the criminal justice system (AME: 0.017, 95 % CI: 0.015, 0.020) and Blacks (AME: 0.004, 95 % CI: 0.000, 0.007) and Hispanics (AME: 0.009, 95 % CI: 0.006, 0.011). ConclusionsAdult use legalization is associated with declining CUD treatment admissions, even though cannabis-related problems are becoming more prevalent. Policies and practices that protect public health, and engage people with CUD in treatment are needed.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Machine learning did not outperform traditional regression models. Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

The Impact of Social Pressure on The Acceptability of Female Students' Dating Practices in Medan City

Objective: This research aims to determine the relationship between social pressure and the permissiveness of dating behavior of high school students in Medan City. This research also analyzes how much influence social pressure has on the permissiveness of dating behavior and analyzes the results based on the reality that occurs in society. Theoretical Framework: Many studies have been carried out regarding permissiveness from various points of view. However, there have not been many studies that specifically explore information related to social pressure as one of the causes of permissive teenage dating behavior. Method: This research uses a quantitative methodology, with linear regression model estimation and correlation analysis, using primary data obtained through questionnaires given to high school students in Medan City, Indonesia. Results and Discussion: The results of the study show that there is a strong relationship between social pressure and the permissiveness of adolescent dating behavior. This is proven by the correlation coefficient between the two variables, which is 0.698. Apart from that, social pressure has a positive influence on the permissiveness of teenagers' dating behavior. Research Implications: This research aims to prove the relationship and influence of social pressure on the permissiveness of adolescent dating behavior. The analysis carried out in this research was not only carried out mathematically but was also linked to theories that have been discussed in previous literature. Originality/Value: This research contributes to the literature by analyzing one of the factors that influence the permissiveness of adolescent behavior. The relevance and value of this research are proven by the testing of the assumption that social pressure is one of the factors causing permissive teenage dating behavior.

Predicting Attitudes Towards the Exchange of Sexual Services for Payment: Variance in Gender Gaps Across the Nordic Countries

Abstract Introduction This article explores how individual-level attitudes towards the exchange of sexual services for payment differ between the Nordic countries. The sparse existing research points to gender and general attitudes towards sexual behavior as powerful predictors of attitudes towards the exchange of sex for payment. However, there are no previous research agendas that attempt to explain variance in such attitudes including all the Nordic countries. Methods We estimate regression models utilizing data from the joint Wave 5 European Values Study (EVS)/World Values Survey (WVS) Wave 7 (EVS 2017; WVS, 2020) asking about the acceptability of prostitution to predict attitudes towards the exchange of sexual services for payment in Denmark, Finland, Iceland, Norway, and Sweden. Results The findings point to considerable variation in attitudes towards the exchange of sex for payment in the Nordic countries. The attitudinal differences align with how the different Nordic governments have approached the issue at hand. Moreover, the results suggest that gender and attitudes towards non-committal casual sex play a critical role in determining attitudes towards the exchange of sex for payment. The impact of attitudes towards non-committal casual sex on attitudes towards the acceptability of exchanging sex for payment is different for women when compared to men in four of the five countries. Conclusions The empirical results provide convincing evidence that women are less likely than men to translate liberal attitudes towards general sexual behavior into lenient attitudes towards the exchange of sex for payment. Policy Implications The results indicate that government policies play a crucial role in shaping public attitudes towards the exchange of sex for payment, and policymakers should consider the potential impact of their stance on the issue. Policymakers and others who want to shift attitudes towards the exchange of sex for payment in the Nordic region should be cognizant of their interconnectedness with gender and attitudes towards non-committal casual sex.

Evaluation of mortality, prognostic parameters, and treatment efficacy in mycosis fungoides.

Mycosis fungoides (MF), the most common primary cutaneous T-cell lymphoma, is characterized by a variable clinical course, presenting either as indolent disease or showing fatal progression due to extracutaneous involvement. Importantly, the lack of prognostic models and predominantly palliative therapy settings hamper patient care. Here, we aimed to define survival rates, disease prediction accuracy, and treatment impact in MF. Hundred-forty MF patients were assessed retrospectively. Prognosis and disease progression/survival were analyzed using univariate Cox proportional hazards regression model and Kaplan-Meier estimates. Skin tumors were linked to shorter progression-free, overall survival and a 3.48 increased risk for disease progression when compared to erythroderma. The Cutaneous Lymphoma International Prognostic Index identified patients at risk in early-stage disease only. Moreover, expression of Ki-67 >20%, CD30 >10%, CD20+, and CD7- were associated with a significantly worse outcome independent of disease stage. Only single-agent interferon-α and phototherapy combined with interferon-α or retinoids/bexarotene achieved long-term disease control in MF. Our data support predictive validity of prognostic factors and models in MF and identified further potential parameters associated with poor survival. Prospective studies on prognostic indices across disease stages and treatment modalities are needed to predict and improve survival.

Bayesian method estimation for Exponential and Weibull Survival regression models

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

An Empirical Social Vulnerability Map for Flood Risk Assessment at Global Scale (“GlobE‐SoVI”)

AbstractFatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the “Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)” at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments.

Penalized least distance estimator in the multivariate regression model

Penalized least distance estimator in the multivariate regression model

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.

On the rate of asymptotic normality of integral weighted kernel estimator in a non parametric regression model for φ-mixing random variables

. In this article, we study the convergence rate of asymptotic normality for the estimator in a non parametric regression model under φ-mixing random variables by using the blockwise technique. With different choices of the parameters, the rates are shown as O ( n − 1 / 9 ) and O ( n − 1 / 6 ) . We also carry out some simulation studies and a real data analysis to support the theoretical results established here.

Nonparametric estimation and forecasting of interval-valued time series regression models with constraints

Nowadays information technology advances allow the collecting and storage of large complex datasets in many areas. Modeling and forecasting interval-valued time series (ITS) has drawn much attention over the last two decades because interval-valued observations contain more information than point-valued observations over the same period and remove undesirable noises in high-frequency data. However, most work mainly focuses on modeling a linear univariate ITS or bivariate point process. This paper proposes nonparametric regression models for interval-valued time series with imposing constraints, e.g., monotonicity. This setting with a monotonic constraint is consistent with the existing literature, which focuses on incorporating valuable empirical information in modeling and forecasts. Two constraint estimators are developed and asymptotic properties are established. Monte Carlo simulation is conducted to show the finite sample performance. An empirical application to equity premium documents that the proposed model yields a better forecast performance than some popular models in the literature.

Temporal trend of anisometropia incidence in Chinese school-aged children before and during the COVID-19 pandemic.

To analyze and compare the temporal trends in the incidence of anisometropia among Chinese school-aged children both before and during the COVID-19 pandemic, and to investigate the impact of the pandemic on the incidence of anisometropia. We conducted a retrospective study comprising six distinct and independent longitudinal cohorts, each including children aged 6 to 13 years who visited the Joint Shantou International Eye Center between January 2010 and December 2021. Children were grouped into cohorts based on the year of their first eye clinic visit: 2010, 2012, 2014, 2016, 2018, or 2020. Only children without anisometropia at initial visits, followed for 18 ± 6 months, were included. The cumulative incidence and risk factors of anisometropia were analyzed using Kaplan-Meier estimation and Cox proportional hazards regression models. Subgroup analyses were performed based on sex, age groups, initial refractive error status, and initial interocular SE difference. Anisometropic children were further categorized into myopic and non-myopic, with subsequent subgroup analyses conducted. Of 11,235 children were recruited from six cohorts (2010: n = 1,366; 2012: n = 1,708; 2014: n = 1,896; 2016: n = 2,354; 2018: n = 2,514; 2020: n = 1,397), 869 children developed anisometropia during a mean follow-up of 17.5 ± 3.7 months. After adjustment of confounding factors, we found that the risk of anisometropia remained relatively stable before 2020 but significantly increased in the 2020 cohort (adjusted HR 2.93, 95% CI 2.23 to 3.86; p < 0.001). This trend persisted in studies of spherical anisometropia (adjusted HR 2.52, 95% CI 1.60 to 3.97; p < 0.001) and cylindrical anisometropia (adjusted HR 2.91, 95% CI 1.69 to 3.62; p < 0.001). Older age and a greater initial difference in SE between the two eyes were also significantly associated with a higher risk of developing anisometropia (p < 0.001). Subgroup analyses consistently showed increased risk in the 2020 cohort. This study reveals a concerning rise in anisometropia incidence among Chinese school-aged children during the period of the COVID-19 pandemic. These findings highlight the worrisome rise in anisometropia risk during the COVID-19 pandemic and emphasize the importance of early detection and management to safeguard children's visual health.

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well.During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI predictionbased on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI.From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them.Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

Application programming interface for electron beam welding of Inconel 718 thin plates

In this paper the simulation of electron beam welding of Inconel 718 thin plates by a moving linear heat source is considered. Neural network models are developed and used for the description of the dependence of the molten pool geometry characteristics on the process parameters - electron beam power, welding velocity, and the plate thickness. Neural network models, based on a multi-layered feedforward neural network, trained with Levenberg-Marquardt error backpropagation algorithm are compared with estimated regression models. The neural networks are trained, tested and validated using a set of experimental data. The resulting models are implemented for developing of an application programming interface for electron beam welding of Inconel 718 thin plates, which is used for prediction, investigation and graphical optimization of the molten pool geometry characteristics of the obtained electron beam welds.

Some strong convergence properties for randomly weighted maximum partial sums of END random variables with statistical applications

This article mainly studies the strong convergence properties for randomly weighted maximum partial sums of arrays of row-wise extended negatively dependent (END, for short) random variables, including the complete moment convergence and complete f-moment convergence. The results obtained in this paper extend the corresponding ones in the literature. As an application, we investigate the complete consistency of the estimators of nonparametric regression models based on END random errors by using the complete convergence that we established. Then we provide comprehensive simulation studies to demonstrate the validity of the theoretical results based on the finite sample performance. Finally, an application to a real data set is illustrated.

Popyt na zróżnicowaną dietę w polskich gospodarstwach domowych

Demand analysis focuses mainly on the study of the relationship between consumption, prices of goods and the changing income of consumers. An attempt aiming to explain the demand for food diversity involves a slightly different approach to research on the determinants of demand. This approach searches for factors that have a statistically significant effect on the quantity and distribution of goods purchased by consumers. The aim of the study described in the article is to determine the factors that have a significant influence on the diversity of the diet in Polish households. The study uses non-identifiable microdata from the 2020 Household Budget Survey conducted by Statistics Poland. Based on the estimation of the quantile regression model, parameter estimates were obtained for the explanatory variables describing the surveyed households. The results confirm that, apart from income and household size, the level of the diversity of a diet depends on the following characteristics: the socio-economic status of the household, place of residence, and the age and education level of the head of the household. The study also shows that the effect of the analysed factors depends on the degree to which the diet is diverse. In general, the impact of the considered factors is more significant for households showing a low demand for a varied diet.

Popyt na zróżnicowaną dietę w polskich gospodarstwach domowych

Demand analysis focuses mainly on the study of the relationship between consumption, prices of goods and the changing income of consumers. An attempt aiming to explain the demand for food diversity involves a slightly different approach to research on the determinants of demand. This approach searches for factors that have a statistically significant effect on the quantity and distribution of goods purchased by consumers. The aim of the study described in the article is to determine the factors that have a significant influence on the diversity of the diet in Polish households. The study uses non-identifiable microdata from the 2020 Household Budget Survey conducted by Statistics Poland. Based on the estimation of the quantile regression model, parameter estimates were obtained for the explanatory variables describing the surveyed households. The results confirm that, apart from income and household size, the level of the diversity of a diet depends on the following characteristics: the socio-economic status of the household, place of residence, and the age and education level of the head of the household. The study also shows that the effect of the analysed factors depends on the degree to which the diet is diverse. In general, the impact of the considered factors is more significant for households showing a low demand for a varied diet.