Traditional Parametric Methods Research Articles

Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Inverse aerodynamic design for distributed propulsion wing with expected circulation distribution

Distributed Propulsion Wing (DPW) technology offers significant advantages in terms of flight energy savings, but the strong aerodynamic coupling between the propulsive internal flow and aerodynamic external flow brings significant design challenges. As the primary DPW profile design is of great significance, this paper proposes a hybrid method to solve the inverse problem mainly based on the formula relationship between the required aerodynamic loads and the profile shape, which is more direct and instructive compared with traditional parametric iterative methods. The aerodynamic characteristics are described by the circulation distribution in the Fourier series form, then the mean camber line of the profile is solved through the re-derived airfoil theory considering disk’s influence. Further CFD correction methods are also proposed. To validate the effectiveness and feasibility of the proposed hybrid inverse method, several DPW profile design tests are then conducted. Finally, the relationship between 2D and realistic 3D unit shape is also researched. The results show that the proposed inverse design method has great accuracy and convergence speed in the design tests, and shows good robustness against changes of the design parameters. The 2D profile shape and the actual 3D shape of DPW unit can establish an aerodynamic-propulsion equivalent relationship based on the same internal mass fluxes.

Intelligent templates to prevent geometric parameterization failure in ship superstructure structural detail design

ABSTRACT Ship superstructures, as typical examples of non-standard design elements, require diverse structural geometric designs and frequent adaptations to meet evolving demands and simulation requirements during the detail design process. However, the utilization of current ship computer-aided design (CAD) systems for detail design presents several limitations, and traditional parametric methods often fall short in complex geometric parameterization. This paper proposed intelligent templates via knowledge-based engineering (KBE) to enhance CAD models' robustness and applicability. The study focused on three specific geometric problems that restrict the validity of parameterization: varying types of input conditions, multiple solutions in offsets and arc fillets, and non-topological transformations. Three representative structural component templates were developed on CATIA V6, showcasing effectiveness compared to traditional methods through a ship design task. Furthermore, this approach provides the basis for the implementation of modular design, design automation, and integrated design simulation.

Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption

Disruptive innovations caused by FinTech (i.e., technology-assisted customized financial services) have brought digital peer-to-peer (P2P) payments to the fore. In this challenging environment and based on theories about customer behavior in response to technological innovations, this paper identifies the drivers of consumer adoption of mobile P2P payments and develops a machine learning model to predict the use of this thriving payment option. To do so, we use a unique data set with information from 701 participants (observations) who completed a questionnaire about the adoption of Bizum, a leading mobile P2P platform worldwide. The respondent profile was the average Spanish citizen within the framework of European culture and lifestyle. We document (in this order of priority) the usefulness of mobile P2P payments, influence of peers and other social groups such as friends, family, and colleagues on individual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (potential) users of mobile P2P payments. We also find that nonparametric approaches based on machine learning algorithms outperform traditional parametric methods. Finally, our results show that feature selection based on random forest, such as the Boruta procedure, as a preprocessing technique substantially increases prediction performance while reducing noise, redundancy of the resulting model, and computational costs. The main limitation of this research is that it only has a place within the sociocultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the effects of the institutional environment on the adoption of mobile P2P payments.

Magnetorheological Damper Force Prediction using Particle Swarm Optimization and Long Short-Term Memory Model

Smart materials, like magnetorheological (MR) fluid, are gaining attention for their ability to rapidly change properties under magnetic influence, making them useful in vibration control systems for vehicles, medical devices, and civil engineering structures. Common parametric models, such as Bouc-Wen and Bingham, are traditionally employed to model MR damper dynamics behavior. However, the manual tuning of numerous parameters in these models increases complexity and hinders the identification of inverse models, potentially leading to unpredictable optimum target forces. In response to these challenges, this study suggested a non-parametric approach using Long Short-Term Memory (LSTM) models to predict the optimum target force of MR dampers. Unlike parametric models, LSTM models capture dynamic behavior without the need for extensive manual tuning. To optimize the LSTM model, Particle Swarm Optimization (PSO) is employed to fine-tune hyperparameter values, ensuring robust performance. The proposed non-parametric method, specifically the PSO-LSTM model, demonstrates faster processing times compared to traditional parametric approaches. The proposed model produced an accurate damping force prediction with a root mean square error of less than 5%, This novel approach simplifies the modeling process and offers an efficient and precise alternative to traditional parametric methods.

Comparing Promotability Outlooks: Industry Professionals vs. MBA Student Perspectives

In career advancement research, understanding the nuanced perceptions of promotability is crucial for both Industry Professionals and MBA students aspiring to navigate the professional landscape. The result of this study delves into the methodological nuances of comparing promotability perceptions between these two distinct cohorts, employing the permutation methods as a robust and effective technique, which confirmed the result of the T-test and Cohens d. The permutations method, a nonparametric approach, excels in situations where traditional parametric methods fall short, especially when dealing with small sample sizes and non-normal distributions. There were 31 students and 27 organizations involved in the study. The High Potential Employees (HiPos) is the theoretical framework used to examine the gap between the rating of Industry professionals and MBA students' self-rating. The researcher is interested in identifying the indicators with the widest gap. Learning agility and leadership spirit were the top two indicators with the most extensive positive difference, while turnover intention was the only indicator with a negative difference. Notably, turnover intention and job engagement were the two indicators with a significant statistical difference in rating between MBA students and Industry Professionals. Also, job engagement depicts the highest Cohen's d size comparison. The result of this study unravels the intricacies of the perceived promotability of MBA students based on their self-rating and the industry professionals' perceptions.

Nonparametric Estimation for High-Dimensional Space Models Based on a Deep Neural Network

With high dimensionality and dependence in spatial data, traditional parametric methods suffer from the curse of dimensionality problem. The theoretical properties of deep neural network estimation methods for high-dimensional spatial models with dependence and heterogeneity have been investigated only in a few studies. In this paper, we propose a deep neural network with a ReLU activation function to estimate unknown trend components, considering both spatial dependence and heterogeneity. We prove the compatibility of the estimated components under spatial dependence conditions and provide an upper bound for the mean squared error (MSE). Simulations and empirical studies demonstrate that the convergence speed of neural network methods is significantly better than that of local linear methods.

Aerodynamic Optimization Framework for a Three-Dimensional Nacelle Based on Deep Manifold Learning-Assisted Geometric Multiple Dimensionality Reduction

As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles.

The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach

This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on labor productivity, apart from the traditional linear relationships and parametric methods employed in previous studies. Examining this topic is imperative for advancing the knowledge on the effects of air pollution on labor productivity and its association with labor insurance, employing a machine learning framework. The results reveal that air pollution, particularly PM10, has a negative impact on labor productivity. Lowering the PM10 level below 36.2 μg/m3 leads to an increase in marginal labor productivity. Additionally, the study identifies labor insurance as a significant factor in improving productivity, with a 9% increase in the total number of labor insurance holders resulting in a substantial 42.9% increase in productivity. Notably, a link between air pollution and insurance is observed, indicating that lower air pollution levels tend to be associated with higher labor insurance coverage. This research holds valuable implications for policymakers, businesses, and industries as it offers insights into improving labor productivity and promoting sustainable economic development.

A nonparametric seismic reliability analysis method based on Bayesian compressive sensing – Stochastic harmonic function method and probability density evolution method

In engineering practice, the evaluation of seismic reliability of high-rise reinforced concrete structure relies on the probabilistic modelling of material properties. However, the measured data of material parameters in engineering practice is usually inadequate to determine the probability model accurately, especially for the problems involving spatial variability. Traditional parametric reliability analysis methods can be biased for this kind of problem. Therefore, a nonparametric seismic reliability analysis method is proposed based on the Bayesian compressive sensing – stochastic harmonic function method and the probability density evolution method. In this method, the conditional random fields are generated and applied to represent material properties of concrete. As a result, the seismic reliability of a high-rise reinforced concrete shear wall model structure is analyzed using the probability density evolution method.

Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier Transform

The extraction of speech features is the basis of speech signal processing, and the extraction of speech features is to obtain the parameters representing speech signals through the analysis of speech signals. The shape information of the signal can be extracted and processed by selecting appropriate structural elements and adopting mathematical morphological transformation. Selecting different structural elements will result in different shape transformation results, thus extracting the shape information of different components. In this paper, the feature extraction and analysis of speech signals are further studied based on FRFT (fractional fourier transform). The simulation results show that in different noise environments, such as when the speech is bathed with signal-to-noise ratio of 0dB and 10dB respectively, the recognition rate of this method is higher than that of the traditional parametric method. In this paper, based on the strength of FRFT signal components, the component signals are detected one by one in order, and then according to the detection results, the strongest component signals are removed from the observed signals in order to reduce their influence on the weak component signals, thus improving the effectiveness and reliability of multi-component signal detection and parameter estimation.

Human capital and energy consumption: Six centuries of evidence from the United Kingdom

We examine the relationship between human capital and energy consumption in the United Kingdom employing time series data dating back to the beginning of the sixteenth century. We first employ traditional parametric methods to examine the long-run effects. We find a negative relationship between total human capital and energy consumption in the long run, with an additional year of schooling reducing energy consumption in the range 4–9%, depending on the identification method. Given that such a long time series contains non-linearities and structural shifts in the data, we investigate the non-linear properties and find a long-run relationship between human capital and energy consumption. We also relax the functional form assumptions and utilise local linear non-parametric regression. The results show a time-varying non-parametric link between human capital and energy consumption, although, consistent with the linear long-run estimates, the relationship is negative.

A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.

Applying fuzzy inference system and analytic network process based on GIS to determine land suitability potential for agricultural.

Determining soil characteristics and land limitations through the land suitability assessment process is a very important step, due to its important role in sustainable management and reducing the effects of land degradation for the continuous production of agricultural crops. In this study, land suitability potential for wheat cultivation was determined by analytic network process (ANP)-fuzzy and traditional parametric methods including Storie and square root in lands with an area of about 4300ha located in northwestern Iran. For this purpose, according to the land use maps, topography, slope, geology, and satellite image of the study area, the initial soil units were identified and soil was sampled by digging profiles in these units from the soil horizons of the profiles. According to the results, the ANP-fuzzy method had a more accurate evaluation in determining the land suitability potential, based on the determination coefficient (R2) value between wheat yield and land suitability index compared to parametric methods. The application of ANP-fuzzy method determined 2328.05ha, 53.79% of land highly suitable (S1), 1577.69ha, 36.45% moderately suitable (S2), 419.6ha, 9.69% marginally suitable (S3), and 3.02ha, 0.07% currently unsuitable (N1). The most important limiting characteristics were slope, soil depth, ESP, Gypsum, pH, and soil salinity. Therefore, due to optimal weighting of characteristics, determining suitable limits of critical points, and applying ideal fuzzy membership functions, the ANP-fuzzy method can be applied as a superior method for assessing land potential for sustainable agricultural crop production in other parts of the world.

Convenient design method for customized implants based on bionic vein structure features.

Matching implants to bones is crucial for customized orthopedic medicine. Existing methods for designing customized implants predominantly adopt the parameterized deformation method that uses a fragmented representation of semantic parameters. Such a representation cannot provide information integration management and therefore restricts the retrieval of information regarding implant features and the improvement of customized design efficiency. Therefore, this study proposes a rapid design method for customized implants based on bionic vein structure features. First, a bionic vein structure was designed to represent the implant type. Second, the bionic vein structure was represented by a digraph structure with morphological and dimensional features. Finally, the implant model was rapidly built by retrieving the sketch and other modeling operations. Common implants such as the T-shaped plate, L-shaped plate, clover plate, and femoral stem prosthesis were used as explanations or test cases. The experimental work shows that combining the traditional parametric deformation method with bionic vein structure features in our present method is flexible and efficient results, and can improve the efficiency of customized implant design.

Scenario Generation for Wind Power Using Generative Adversarial Networks

Scenarios generation is a critical part in planning and operation in high renewable energy penetratied power systems. However, the statistical assumptions of traditional parametric methods may not hold for all types of wind farms. In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs). Unlike traditional probabilistic model-based techniques which are typically difficult to scale or sample, the proposed method is data-driven and captures patterns of wind power generation. First, the GAN network structure is constructed, and the Wasserstein distance is employed as the discriminator’s loss function. The GAN training then enables the generator to learn random noise and actual history data. Finally, the scenario generation approach based on Monte Carlo simulation and GANs are compared. It shows that the scenarios generated by proposed method can accurately describe the uncertainty of wind power output.

Modeling and optimization for multiple correlated responses with distribution variability

In production design processes, multiple correlated responses with different distributions are often encountered. The existing literature usually assumes that they follow normal distributions for computational convenience, and then analyzes these responses using traditional parametric methods. A few research papers assume that they follow the same type of distribution, such as the t-distribution, and then use a multivariate joint distribution to deal with the correlation. However, these methods give a poor approximation to the actual problem and may lead to the recommended settings that yield substandard products. In this article, we propose a new method for the robust parameter design that can solve the above problems. Specifically, a semiparametric model is used to estimate the margins, and then a joint distribution function is constructed using a multivariate copula function. Finally, the probability that the responses meet the specifications simultaneously is used to obtain the optimal settings. The advantages of the proposed method lie in the consideration of multiple correlation patterns among responses, the absence of restrictions on the response distributions, and the use of nonparametric smoothing to reduce the risk of model misspecification. The results of the case study and the simulation study validate the effectiveness of the proposed method.

Non-parametric multiple inputs prediction model for magnetic field dependent complex modulus of magnetorheological elastomer

This study introduces a novel platform to predict complex modulus variables as a function of the applied magnetic field and other imperative variables using machine learning. The complex modulus prediction of magnetorheological (MR) elastomers is a challenging process, attributable to the material’s highly nonlinear nature. This problem becomes apparent when considering various possible fabrication parameters. Furthermore, traditional parametric modeling methods are limited when applied to solve larger-scale cases involving large databases. Consequently, the application of non-parametric modeling such as machine learning has gained increasing attraction in recent years. Therefore, this work proposes a data-driven approach for predicting multiple input-dependent complex moduli using feedforward neural networks. Besides excitation frequency and magnetic flux density as operating conditions, the inputs consider compositions and curing conditions represented by magnetic particle weight percentage and the curing magnetic field, respectively. Extreme learning machines and artificial neural networks were used to train the models. The simulation results obtained at various curing conditions and other inputs confirm that the predicted complex modulus has high accuracy with an R2 of about 0.997, as compared to the experimental results. Furthermore, the predicted complex modulus pattern and magnetorheological effect agree with the experimental data using both the learned and unlearned data.

Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner

Causal inference under the potential outcome framework relies on the strongly ignorable treatment assumption. This assumption is usually questionable in observational studies, and the unmeasured confounding is one of the fundamental challenges in causal inference. To this end, we propose a new sensitivity analysis method to evaluate the impact of the unmeasured confounder by leveraging ideas of doubly robust estimators, the exponential tilt method, and the super learner algorithm. Compared to other existing methods of sensitivity analysis that parameterize the unmeasured confounder as a latent variable in the working models, the exponential tilting method does not impose any restrictions on the structure or models of the unmeasured confounders. In addition, in order to reduce the modeling bias of traditional parametric methods, we propose incorporating the super learner machine learning algorithm to perform nonparametric model estimation and the corresponding sensitivity analysis. Furthermore, most existing sensitivity analysis methods require multivariate sensitivity parameters, which make its choice difficult and subjective in practice. In comparison, the new method has a univariate sensitivity parameter with a nice and simple interpretation of log-odds ratios for binary outcomes, which makes its choice and the application of the new sensitivity analysis method very easy for practitioners.