Local Regression Method Research Articles

Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.

Retracted: Evaluation and Numerical Simulation of Music Education Informationization Based on the Local Linear Regression Method

Retracted: Evaluation and Numerical Simulation of Music Education Informationization Based on the Local Linear Regression Method

A Study on the Enhancement of English Proficiency of Contemporary College Students by Intercultural Communication in the Background of the Internet

Abstract The process of English learning is closely related to the process of intercultural communication. This paper addresses the problem of quantifying the intercultural communication ability of college students, designs the dimensions of intercultural competence, establishes the factor indicators of intercultural competence, and constructs a fuzzy comprehensive evaluation method for the assessment of intercultural competence. A regression model is created to investigate the correlation between achievement and English proficiency. The nonparametric model of principal component analysis is used to optimize the problem of the stratification effect of general local linear regression, and the principal component-based local linear regression method (PC-LLR) is proposed. The second-year undergraduate students of different majors in X university were selected as the research subjects, and an overall assessment of intercultural communication competence was made to explore the differences among different student groups. Based on the correlation analysis between intercultural competence and English proficiency, PC-LLR regression analysis was launched. The Beta value interval of the regression was [0.0535, 5.0292], all of which were positive, and the four variables of knowledge, attitude, skill, and awareness had a positive predictive effect on the dependent variable. Intercultural knowledge has a relatively high Beta value, which makes it a strong predictor of English proficiency.

Estimation of soil organic carbon content by Vis-NIR spectroscopy combining feature selection algorithm and local regression method

ABSTRACT Soil organic carbon (SOC) content is a critical parameter for evaluating soil health. However, high redundancy and invalid information in soil hyperspectral data can reduce the accuracy and stability of SOC prediction models. This study developed a global partial least squares regression (PLSR) model and a local PLSR model for agricultural soils in the LUCAS 2015 database. Some variable selection methods were combined with the regression models and their effects on prediction accuracy were explored. In addition, when the genetic algorithm is utilized for spectral feature selection, we obtained a more representative spectral subset through a novel coding approach. The results illustrated that the best SOC estimation accuracy was achieved by the local PLSR combined with a coding-improved genetic algorithm (GA), with R 2 of 0.71, RMSEP of 5.7 g kg -1 , and RPD of 1.87. This study demonstrates that appropriate spectral band selection only slightly enhances the model performance of both global and local regressions, as PLSR models using the full spectrum show similar performance. Local PLSR models consistently outperform global ones using full spectrum or variable selection algorithms.

Methods to get more information from sparse vessel monitoring systems data

Vessel Monitoring Systems (VMS) and other vessel tracking data have been used for many years to map the distribution of fishing activities. Mapping areas with low levels of fishing activity can be of particular interest; for example to avoid conflicts between fishing and other ocean uses like offshore renewable energy or to protect relatively pristine ecosystems from increasing fishing pressure. A particular problem when trying to delineate areas that are lightly fished, is the relative sparsity of vessel monitoring data in these areas. This paper explores three novel methods for estimating the distribution of fishing activity from VMS data, with particular focus on lightly impacted areas. The first new method divides the area of interest into a nested grid with varying cell sizes (depending on the density of data at each location); the second new method uses Voronoi diagrams to define polygons around observations and the third method applies a local regression to generate a smooth map of fishing intensity. The new methods are compared with two established methods: applying spatial grids and interpolating fishing tracks. The track interpolation method generally performs better than any of the new methods, however it is not always possible or appropriate to apply track interpolation; in those cases the local regression method is the best alternative.

Relation between aging intensity function and WPP plot and its application in reliability modelling

Parameter estimation on heavily censored data is a challenging problem, and new methods are needed. This paper aims to address this issue. It is based on a relation between the Weibull probability paper (WPP) plot and the aging intensity function (AIF): the slope of tangent line of the WPP plot is equal to the AIF. Based on this property, a WPP-based local regression method is developed to obtain the empirical AIF, which can aid model selection; and a WPP-based global regression method is proposed to obtain a sample of the shape parameter of a distribution, from which the shape parameter is estimated. Once this is done, the point estimate of the shape parameter is fixed and a single-parameter maximum likelihood method is used to estimate the scale parameter. The proposed method is applicable for both the Weibull and non-Weibull distributions. Two examples are included to illustrate the proposed method and its appropriateness. The results show that the proposed method outperforms the existing methods in terms of applicability, simplicity, accuracy, robustness and unbiasness.

Event-triggered fuzzy control for vehicle nonlinear suspension systems with time delay

This article investigates the adaptive event-triggered fuzzy control problem for vehicle suspension systems with nonlinear characteristics and time delay. First, to describe the vehicle suspension system with nonlinear components, a Takagi-Sugeno fuzzy suspension model is established via local linear regression method. Second, considering that the communication bandwidth is limited, an adaptive event-triggered strategy is employed to acquire a higher efficiency in terms of saving the transmission resource. Third, based on a time-delay principle and Lyapunov stability theory, a co-design method is proposed to obtain the event generator and the fuzzy controller simultaneously. The proposed event-triggered fuzzy control method is effective to improve performances of the suspension system and guarantee the limits of suspension travel and tire deflection. Finally, compared with the existing method, the effectiveness of the proposed method is verified by simulation tests with various road disturbances.

Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms

Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images.

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model.

Correlations between socioeconomic factors and poverty in regression models do not reflect actual relationships, especially when data exhibit patterns of spatial heterogeneity. Spatial regression models can estimate the relationships between socioeconomic factors and poverty in defined geographical areas, explaining the imbalanced distribution of poverty, but the relationships between these factors and poverty are not always linear however, and conventional simple linear local regression models do not accurately capture these nonlinear relationships. To fill this gap, we used a local regression method, geographically weighted random forest regression (GW-RFR), that integrates a spatial weight matrix (SWM) and random forest (RF). The GW-RFR evaluates the spatial variations in the nonlinear relationships between variables. A county-level poverty data set of China was employed to estimate the performance of the GW-RFR against the random forest (RF). In this poverty application, the value of {R}^{2} was 0.128 higher than that of the RF, the NRMSE value was 1.6% lower than the RF, and the MAE value was 0.295 lower than the RF. These results showed that the relationship between poverty factors and poverty varies with space at the county level in China, and the GW-RFR was suitable for dealing with nonlinear relationships in local regression analysis.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-17513-3.

Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.

An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions

For a global breeding organization, identifying the next generation of superior crops is vital for its success. Recognizing new genetic varieties requires years of in-field testing to gather data about the crop’s yield, pest resistance, heat resistance, etc. At the conclusion of the growing season, organizations need to determine which varieties will be advanced to the next growing season (or sold to farmers) and which ones will be discarded from the candidate pool. Specifically for soybeans, identifying their relative maturity is a vital piece of information used for advancement decisions. However, this trait needs to be physically observed, and there are resource limitations (time, money, etc.) that bottleneck the data collection process. To combat this, breeding organizations are moving towards advanced image capturing devices. In this paper, we develop a robust and automatic approach for estimating the relative maturity of soybeans using a time series of UAV images. An end-to-end hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) is proposed to extract features and capture the sequential behavior of time series data. The proposed deep learning model was tested on six different environments across the United States Results suggest the effectiveness of our proposed CNN-LSTM model compared to the local regression method. Furthermore, we demonstrate how this newfound information can be used to aid in plant breeding advancement decisions.

Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics

Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites.

Application of Bayesian network and regression method in treatment cost prediction

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Evaluation and Numerical Simulation of Music Education Informationization Based on the Local Linear Regression Method

The study of this theoretical problem enables sparse or dense functional data, including educational information evaluation data. The choice of different weights is subjected to principal component analysis. The evaluation of music education informatization level mainly evaluates the status quo of music education informatization development, provides a basis for formulating and adjusting music education informatization development policies, and provides support for educational decision-making, to promote the sustainable and balanced development of music education informatization. The evaluation of music education informatization has become the key promotion work of music education informatization at this stage. This paper studies the convergence rate of functional principal components based on the local linear method under general weighting conditions. First, we introduce the related research on the estimation of mean and covariance function under general weighting. Secondly, for principal functional components under general weighting, namely, eigenvalues and eigen functions, the text gives the corresponding estimated values and derives its strong uniform convergence rate. Finally, the convergence rate was verified by simulation research. The estimation methods and conclusions of this article enrich the research of functional linear regression models and will help analyze the complex and changeable problems encountered in the application of music education information.

Benchmarking for using an acoustic Doppler current profiler for bathymetric survey.

This research aims to improve data post-processing from an acoustic Doppler current profiler (ADCP) to obtain additional depth information with a high-quality bathymetric result. To validate the depth, dataset in a control area was used a scaled rod, the RTK (Real-Time Kinematic) positioning technique, and a single beam echo sounder (SBES). The developed post-processing and validation in the control area applied to a small region of a water supply reservoir in Brazil. A robust local regression method eliminated outliers from the raw data. Subsequently, a linear regression model was generated for the beams from the ADCP to adjust depths and improve the dataset. The statistical hypothesis test performed before and after the adjustment supported the validation of data processing. The resulting volumes calculated from the ADCP raw data showed a difference of 30% with SBES, which is usually the standard equipment used for bathymetric surveys. The proposed post-processing techniques declined to <1% of the volume discrepancy. The raw dataset standard deviation was 0.15m on average for Doppler beams, and after adjusts, this value dropped to 0.03m. The results showed the necessity of a post-processing method to improve depth data quality from ADCP. Thus, the procedure described in this document can be used for bathymetric surveys using ADCP for analysis concerning the estimation of reservoir volume.

Double smoothing local linear estimation in nonlinear time series

We generalize the double smoothing local linear regression method to nonparametric regression of time series. Under a strong mixing condition for the dependence of the time series, we show that after another round of smoothing based on the local linear regression estimates, the double smoothing local linear estimate will have reduced asymptotic bias, while keeping the variance at the same asymptotic order. The asymptotic bias reduces from the order of h 2 for the local linear estimates to h 4 for the double smoothing local linear estimates, where h is the bandwidth. Hence the double smoothing local linear method produces more optimal estimates in terms of mean squared error. Simulation studies and real time series data analysis confirm the advantages of the double smoothing method compared to the local linear method.

Using Z-score to optimize population-specific DDH screening: a retrospective study in Hangzhou, China

BackgroundDDH (Developmental Dysplasia of the Hip) screening can potentially avert many morbidities and reduce costs. The debate about universal vs. selective DDH ultrasonography screening in different countries revolves to a large extent around effectiveness, cost, and the possibility of overdiagnosis and overtreatment. In this study, we proposed and evaluated a Z-score enhanced Graf method to optimize population-specific DDH screening.MethodsA total of 39,710 history ultrasonography hip examinations were collected to establish a sex, side specific and age-based Z-scores model using the local regression method. The correlation between Z-scores and classic Graf types was analyzed. Four thousand two hundred twenty-nine cases with follow-up ultrasonographic examinations and 5284 cases with follow-up X-ray examinations were used to evaluate the false positive rate of the first examination based on the subsequent examinations. The results using classic Graf types and the Z-score enhanced types were compared.ResultsThe Z-score enhanced Graf types were highly correlated with the classic Graf’s classification (R = 0.67, p < 0.001). Using the Z-scores ≥2 as a threshold could reduce by 86.56 and 80.44% the false positives in the left and right hips based on the follow-up ultrasonographic examinations, and reduce by 78.99% false-positive cases based on the follow-up X-ray examinations, respectively.ConclusionsUsing an age, sex and side specific Z-scores enhanced Graf’s method can better control the false positive rate in DDH screening among different populations.

Mortality forecasting using factor models: Time-varying or time-invariant factor loadings?

Many existing mortality models follow the framework of classical factor models, such as the Lee–Carter model and its variants. Latent common factors in factor models are defined as time-related mortality indices (such as κt in the Lee–Carter model). Factor loadings, which capture the linear relationship between age variables and latent common factors (such as βx in the Lee–Carter model), are assumed to be time-invariant in the classical framework. This assumption is usually too restrictive in reality as mortality datasets typically span a long period of time. Driving forces such as medical improvement of certain diseases, environmental changes and technological progress may significantly influence the relationship of different variables. In this paper, we first develop a factor model with time-varying factor loadings (time-varying factor model) as an extension of the classical factor model for mortality modelling. Two forecasting methods to extrapolate the factor loadings, the local regression method and the naive method, are proposed for the time-varying factor model. From the empirical data analysis, we find that the new model can capture the empirical feature of time-varying factor loadings and improve mortality forecasting over different horizons and countries. Further, we propose a novel approach based on change point analysis to estimate the optimal ‘boundary’ between short-term and long-term forecasting, which is favoured by the local linear regression and naive method, respectively. Additionally, simulation studies are provided to show the performance of the time-varying factor model under various scenarios.

A kernel regression model for panel count data with time-varying coefficients

For the conditional mean function of panel count model with time-varying coefficients, we propose to use local kernel regression method for estimation. Partial log-likelihood with local polynomial is formed for estimation. Under some regularity conditions, strong uniform consistency rates are obtained for the local estimator. At target time point, we show that the local estimator converges in distribution to normal distribution. The baseline mean function estimator is also shown to be consistent. Simulation studies show that the time-varying coefficient estimator is close to the true value, the empirical coverage probabilities of the confidence interval is close to the nominal level. We also applied the proposed method to analyze a clinical study on childhood wheezing.