Fuzzy Regression Research Articles

Understanding the Effects of a Math Placement Exam on Calculus 1 Enrollment and Engineering Persistence

Educational institutions are grappling with declining enrollments and low mathematical achievements. This study investigates how a math placement exam (ALEKS) influences enrollment in Calculus 1 and student persistence, taking into account academic preparation and demographic factors. It also evaluates the effects of remedial math courses for students near the placement cutoff. Using Astin’s input–environment–outcome model, this study analyzed data from 3380 students employing a Kitagawa-Oaxaca-Blinder decomposition and fuzzy regression discontinuity. These methods were used to identify unexplained differences across demographic groups and capture outcomes near the math placement cutoff. Based on the findings, a cutoff of 80% for the ALEKS exam is appropriate. This study underscores the role of math placement exams in shaping engineering enrollment and student success. These findings prompt reevaluating placement strategies and support mechanisms, particularly for URM, first-generation, and female students, to enhance equity and retention in engineering.

The impact of spousal retirement on health—an empirical analysis based on CFPS

IntroductionHealth impacts associated with spousal retirement vary significantly across countries and datasets, underscoring the need to examine this relationship within the Chinese context. This study aims to explore how spousal retirement affects individual health in China.MethodsUtilizing data from the China Family Panel Studies (CFPS) for the years 2014, 2016, 2018, and 2020, this research applies a fuzzy regression discontinuity approach to assess the effects of spousal retirement on health outcomes.ResultsFindings indicate that a wife’s retirement positively impacts her husband’s mental health but negatively affects his physical health. Conversely, a husband’s retirement improves his wife’s self-rated health. Further analysis reveals that a husband’s retirement significantly enhances his wife’s marital satisfaction, contributing to longer sleep durations and, subsequently, better self-rated health.DiscussionThis study highlights the nuanced effects of spousal retirement on individual health, with implications for understanding the marital and health dynamics within aging populations in China.

A Two-way Crossed Effects Fuzzy Panel Linear Regression Model

Over the last two decades, the panel data model has become a focus of applied research. While there are numerous proposals for soft regression models in the literature, only a few linear regression models have been proposed based on fuzzy panel data. However, these models have serious limitations. This study is an attempt to propose a kind of two-way fuzzy panel regression model with crossed effects, fuzzy responses and crisp predictors to overcome the shortcomings of these models in real applications. The corresponding parameter estimation is provided based on a three-step procedure. For this purpose, the conventional least absolute error technique is employed. Two real data sets are analyzed to investigate the fitting and predictive capabilities of the proposed fuzzy panel regression model. These real data applications demonstrate that our proposed model has good fitting accuracy and predictive performance.

Climate change impact on water treatment plants: analysis of chlorophyll-a levels and process performance.

Climate change significantly impacts the risk of eutrophication and, consequently, chlorophyll-a (Chl-a) concentrations. Understanding the impact of water flows is a crucial first step in developing insights into future patterns of change and associated risks. In this study, the Statistical DownScaling Model (SDSM)-a widely used daily downscaling method-is implemented to produce downscaled local climate variables, which serve as input for simulating future hydro-climate conditions using a hydrological model. The vulnerability of water quality, particularly Chl-a concentrations in the Latyan Dam and Tehranpars Water Treatment Plant (TWTP) is assessed through six fuzzy regression models under three scenarios: RCP2.6, RCP4.5, and RCP8.5. Projections indicate an increase in minimum temperatures for the Jajrood watershed ranging from 92 to 93%. Seasonal forecasts suggest significant precipitation during the dry season. The HYMOD model predicts increases in streamflow of approximately 97%, 90%, and 92% by 2050 under RCP2.6, RCP4.5, and RCP8.5, respectively, indicating a heightened risk of flooding that poses economic, health, and environmental concerns. Among the six fuzzy regression models, FGR1, FGR3, and FGR4 demonstrated the most favorable results in modeling Chl-a output from the TWTP. In conclusion, while Chl-a concentrations in the effluent of the TWTP are only slightly influenced by climate change, the effects on streamflow patterns are significant. These findings highlight serious future water quality challenges and increased vulnerability of water resources due to climate change.

Estimation of Fuzzy Regression Parameters With ANFIS and Bayesian Methods

ABSTRACTIn fuzzy regression, estimation of centers and spreads of triangular fuzzy numbers are both performed using the same methods, such as linear programming (LP), quadratic programming (QP), fuzzy weighted linear programming (FWLP), and adaptive neuro‐fuzzy inference system (ANFIS). In this article, ANFIS and Bayesian methods have been adopted to estimate centers and spreads of a triangular fuzzy numbers are applied, respectively, for estimation of centers and spreads of the triangular fuzzy data. To illustrate the efficiency of the proposed approach, three numerical examples have been used to verify the superiority of proposed method to other existing ones.

Erasmus program and labor market outcomes: Evidence from a fuzzy regression discontinuity design

Erasmus program and labor market outcomes: Evidence from a fuzzy regression discontinuity design

Enrollment in Medicare is associated with fewer outpatient mental healthcare visits among those with mental health symptoms.

To test whether enrolling in traditional Medicare (TM) or Medicare Advantage (MA) at age 65 reduces mental healthcare utilization among individuals with mental health symptoms and low or moderate family incomes. We employ a fuzzy regression discontinuity design, comparing the likelihood of having an outpatient mental health visit or a psychotropic drug fill among individuals younger than or older than the age 65 Medicare eligibility threshold. We analyze 2014-2021 Medical Expenditure Panel Survey data. Our primary sample is restricted to individuals with probable mental health symptoms as indicated by their score on the Kessler K6 psychological distress scale (K6) and Patient Health Questionnaire-2 instrument (PHQ-2) and who have incomes less than 400% of the federal poverty level. Among individuals with probable mental health symptoms and low or moderate incomes, enrolling in Medicare (combining the effect of MA and TM) is associated with a 24.9 percentage point reduction (95% CI -49.1 to -0.8; p = 0.043) in the likelihood of having any type of outpatient mental health visit and a 31.3 percentage point reduction (95% CI -54.2 to -8.4; p = 0.008) in the likelihood of having a prescription drug fill for a psychotropic drug. Effects of MA and TM on mental healthcare utilization are not statistically different from each other. We observe no impact of enrolling in Medicare on the likelihood of having a visit to a primary care provider, having a visit to a non-mental healthcare specialist, or having a fill for a prescribed non-psychotropic drug. Enrolling in Medicare is associated with a reduction in the use of mental healthcare among individuals with probable mental health symptoms and low or moderate family incomes. Our findings suggest that the program poses access barriers specific to mental healthcare.

Rural energy poverty alleviation in OECD nations: An integrated analysis of renewable energy, green taxation, and the United Nations agenda 2030

Energy poverty in the Organisation for Economic Co-operation and Development (OECD) countries is a complex issue that needs careful and targeted solutions. Tens of millions of households in OECD countries struggle to pay for enough electricity and heating to meet their basic needs. Thus, it is essential to make a detailed assessment of rural energy poverty with an integrated approach. In so doing, we first measure rural energy poverty composite index across 38 OECD countries and their 5 major partners from 2000 to 2021. We then explored the linear effect of renewable energy technologies on energy poverty alleviation. Moreover, the mediating effect of green taxes on rural energy poverty through renewable energy technologies was analysed. Finally, the fuzzy regression discontinuity (FRD) method is employed to address the endogeneity issue associated with the assignment of the 2030 United Nations (UN) Agenda and to identify treatment effects. The results show that rural energy poverty improves over time. However, significant spatiotemporal disparities in alleviation efforts are found across countries. The results of Method of Moment Quantile Regression (MMQR) approach reveal that renewable energy technologies can accelerate rural energy poverty mitigation in the sample countries. We find that green taxation is an important mediating channel through which the adoption of renewable energy technologies alleviates rural energy poverty. The results of the Fuzzy-RD show that the 2030 UN agenda was effective in increasing the likelihood of rural energy poverty mitigation. The results remain consistent after a battery of robustness tests. This study offers valuable insights that can aid policymakers in formulating robust public policies.

Relationship between a diagnosis of kidney failure and heart diseases in patients with hyperkalemia

Objectives This study seeks to determine the association between kidney failure and heart diseases by examining how they influence the diagnosis of hyperkalemia. Methods We employ a fuzzy regression discontinuity design (RDD) by harnessing the inherent threshold in potassium levels, which serves as a diagnostic criterion for hyperkalemia. Simultaneously, we utilize patient diagnosis data related to kidney failure and heart diseases. This approach allows us to evaluate the causal impact of both diagnoses on hyperkalemia. Results Significant overall increases in the risk of developing hyperkalemia are evident subsequent to a diagnosis of kidney failure or heart disease. The study finds that the probability of receiving a kidney failure diagnosis increases by 11.2% regarding a cut-off of 6 mEq/L of potassium. In addition, there is a 6.8% likelihood of experiencing hyperkalemia in the case of a prior diagnosis of hypertension, and an 8.8% probability in the case of a diagnosis of depression. The findings remain robust when considering alternative parametric and non-parametric specifications as well as placebo tests. Conclusions This study provides new empirical insights into the causal impact of kidney failure and heart disease, underscoring the significance of monitoring such patients to prevent serious complications in the future.

Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models

Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models

High-flow nasal cannulas versus standard oxygen therapy for moderate bronchiolitis: a quasi-experimental analysis.

In the last decades none of the medical therapies investigated have shown clear efficacy in the treatment of bronchiolitis, and literature agrees on a general de-implementation of pharmacological therapies, recognizing an effective role only to nutritional support and oxygen therapy. High-flow nasal cannulas (HFNC) has become increasingly popular in the last decade, despite its lack of clear efficacy. Recent randomized controlled trials (RCT) comparing standard oxygen therapy (SOT) and HFNC did not demonstrate significant benefit of HFNC. To acquire more clinical data on HFNC efficacy we performed a retrospective, quasi-experimental analysis of patients admitted for bronchiolitis in the epidemic seasons 2021-2022 and 2022-2023. To assess the efficacy of SOT and HFNC we used a pragmatic approach, a fuzzy regression discontinuity design, which is a quasi-experimental test. Unlike RCTs, this process is not a true randomization, but may be interpreted as quasi-randomization in an observational setting. HFNC did not reduce length of oxygen therapy (LOO) nor length of hospitalization (LOS) (respectively, p: 0.383 and p: 0.454). Treatment failure was not significantly different in the treatment groups (p: 0.354). It is crucial to perform additional RCTs with uniform protocols to determine the efficacy of HFNC more accurately in the treatment of bronchiolitis. HFNC does not reduce LOO, suggesting that early use of HFNC does not change the course of disease in moderate bronchiolitis. In view of the greater complexity and higher cost, HFNC should not be routinely used as first-line treatment in children with moderate respiratory distress and mild hypoxemia.

Cross‐domain policy feedback effects on mass publics

AbstractScholars have extensively explored policy feedback effects on mass publics. However, research exploring how one policy's implementation affects public preferences for another remains limited. This study proposes a novel cross‐domain policy feedback framework, emphasizing functional and normative interdependence as two mechanisms yielding cross‐domain resource and interpretive effects. We use multiple sets of nationally representative survey data and archival records from China, a context deeply rooted in filial piety culture. We employ a fuzzy regression discontinuity design and multilevel regressions to explore the effects of the one‐child policy on public preferences for old‐age support. The findings indicate that individuals subjected to more stringent enforcement exhibit increased support for the government assuming the responsibility for old‐age support. These changes largely stem from the reduction in their actual and desired number of children. The findings have significant implications for mass feedback research and reform efforts regarding population planning and public pension systems.

Non-Parametric Fuzzy Regression Discontinuity Design in Modelling Viral Load Suppression Among People Living With HIV/AIDS (PLWHA)

Adherence to antiretroviral therapy (ART) is a significant determinant of viral load suppression in HIV patients. There are inadequate statistical models that bring out the direct effects of ART on the suppression of HIV/AIDS. Traditional regression models address the general determinants of viral load suppression. Regression discontinuity designs, on the other hand, bring out the causal effects of ART on viral load suppression based on various thresholds. This study used the non-parametric fuzzy regression discontinuity design (FRDD) to model viral load suppression in PLWHA. The study began with developing a non-parametric FRDD, simulating the model to assess its performance, and applying the model to the Quality-of-Care dataset from Kaggle. The study focused on viral load suppression as the outcome variable, CD4 count and age as the running variables, gender, and whether a patient received counseling as additional covariates. The optimal thresholds were 40.5 years and 320 cubic millimeters for the CD4. There was an increasing negative treatment effect of ART on viral load suppression as the cutoff points for CD4 count increase. At the same time, there was an increasing negative treatment effect of ART on viral load suppression with increasing age. The compliance ratios for respondents increased with the negative increase in the treatment effect. Other analyses, such as the McCrary density test, bunching test, and manipulation test, indicated that the non-parametric fuzzy regression discontinuity design is effective in modeling viral load suppression.

Fuzzy robust regression based on exponential-type kernel functions

The least squares method is a frequently used technique in fuzzy regression analysis. However, it is highly sensitive to outliers in the dataset. To address this challenge, we propose a novel robust fuzzy regression model based on exponential-type kernel functions. This approach effectively mitigates the influence of poorly fitted observations on the predicted results by reducing their weights. Furthermore, we use the gh-transformation to guarantee the nonnegativity of the spreads of the predicted response variable. In order to evaluate the performance of our method, a simulation study and three real data sets were considered. The experimental results demonstrate that the proposed method outperforms several popular robust methods in almost all cases. Furthermore, a sensitivity analysis of the estimated parameters provides further evidence of the superior efficiency of the proposed method.

Does parents' retirement influence the fertility intentions of subsequent generations among internal migrant populations? Evidence from China.

The fertility health of the migrating population has attracted significant attention. This article examines the impact of parental retirement on the fertility intentions of the offspring among the internal migrant population. This study utilizes the mandatory retirement age system as an exogenous shock within a fuzzy regression discontinuity (FRD) experimental design to investigate the influence of parents' retirement on the fertility intentions of their migrant offspring and potential mechanisms. The research findings indicate that parents' retirement significantly reduces the fertility intentions of the migrant population by 34.4%. Mechanism analysis attributes this adverse effect to the reduction in intergenerational wealth transfer due to parent's retirement. As the family's economic situation worsens, the negative impact of retirement on fertility intentions becomes more pronounced. The childcare support mechanism provided by retired grandparents can partially offset the adverse effects of the income mechanism. This study provides recommendations for enhancing policies related to delaying retirement age and childcare.

Adjustment of the Height Triangular Fuzzy Regression as Early Awareness of Breast Cancer

Breast cancer is the most popular malignancy among Malaysian women. In Malaysia, approximately one in every 19 women is at risk. Breast cancer is the most common cancer among Malaysian women (32.9%), followed by colorectal (11.9%) and ovarian cancers (7.2%). Many cases are found to be advanced, with considerable tumour development or metastasis to untreatable locations and unaware of the factors. This study aims to determine the best height triangular fuzzy regression model by adjustment between 0 – 1 and measure the value of statistical error using mean square error (MSE) and root mean square error (RMSE). Secondary data was used where 569 patients having BREAST cancer and receiving treatment in hospitals was recorded by nurses and doctors. The patient data for breast cancer were analysed using MATLAB, SPSS, and Microsoft Excel. Based on the results, H = 0 of fuzzy linear regression is the best model to predict the breast cancer awareness with lowest value of MSE and RMSE by 1.455 and 1.206 respectively. Malaysians must be aware of the warning factors of breast cancer to increase survival and minimise death rates.

Fuzzy Regression Discontinuity Designs With Multiple Control Groups Under One-Sided Noncompliance: Evaluating Extended Time Accommodations

Since its conception by Thistlethwaite and Campbell, a regression discontinuity (RD) design has continuously evolved and progressed in various aspects. This paper explores a novel theoretical advancement in fuzzy RD designs by combining them with multiple control groups under one-sided noncompliance. Within this approach, we estimate the average treatment effect on the treated at the cutoff from the fuzzy RD design and construct its bounds derived from multiple control groups. Using the bounds as a sensitivity check, we examine whether the underlying causal or statistical assumptions for the fuzzy RD design are warranted. We verify the effectiveness of our approach via a simulation study and demonstrate its application by studying the effect of extended time accommodations using data from the National Assessment of Educational Progress.

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.

The Forecasting of TheNumber Tourists Arriving in Turkey with Intuitionistic Fuzzy Regression Functions Approach

The impacts of the tourism sector on countries are felt in various areas such as economy, cultural heritage and social development. Tourism contributes significantly to a country's foreign exchange earnings and positively affects the trade network. Tourists' spending boosts local economies and increases employment. These effects are particularly important for Turkey. Tourist visits can be used as a tool for regional promotion. Therefore, tourism demand forecasting is necessary to make the best use of these positive effects on Turkey's economic development and to plan tourism activities. Artificial neural network methods and fuzzy systems for time series forecasting problem are frequently used analysis methods in recent years. In this study, the time series of the total number of tourists visiting Turkey on a monthly basis is analyzed with the intuitionistic fuzzy regression functions approach, which is a generalization of the fuzzy regression functions approach. The analysis performance of the intuitionistic fuzzy regression functions approach is evaluated using fuzzy regression functions approach, multilayer perceptron artificial neural network and multiplicative neuron model artificial neural networks. As a result of the analysis, it is concluded that the intuitionistic fuzzy regression approach produces better forecasting results than both some artificial neural network models and the fuzzy regression functions approach. Since this is the first time that the intuitionistic fuzzy regression functions approach has been used in forecasting the number of tourists, the study aims to contribute to the literature and to help tourism industry employees to be more efficient and successful by providing them with the opportunity to make better future planning.

Like a moth to a flame: Do stock market bubbles exacerbate credit risks of peer‐to‐peer lending?

AbstractWe establish a causal link between stock market bubbles and credit risks from peer‐to‐peer lending. Employing a fuzzy regression discontinuity design based on retail investors' disproportional increase in stock market participation when the Shanghai Stock Exchange composite index exceeds 3500, we find that both the default rate and the degree of delinquency rise disproportionately for loans borrowed above the 3500 threshold. This effect is more pronounced among loans of lower quality and when borrowers are more overconfident and less risk averse. Overall, our results suggest that FinTech developments could amplify financial risks and induce contagion across markets.