Nonparametric Regression Approach Research Articles

The use of the binary spline logistic regression model on the nutritional status data of children

The nutritional status of children can be grouped into two, namely normal and abnormal nutrition so that the data are analyzed using a binary logistic regression model. In this study, the use of binary logistic regression was developed on the use of the spline estimator as part of the nonparametric regression approach. This model is able to model qualitative response data by showing several trends that may occur in the data. Data on the nutritional status of children were analyzed based on the child's weight through a linear spline estimator and the optimal model was obtained at the use of a one-knot point, which was 4.7 kg. These results indicate that if a child has a body weight of 4.7 kg and above then there is a chance that the child has an abnormal nutritional status of 9.013 times compared to a child with a body weight below 4.7 kg. This could be due to the fact that children are no longer getting breast milk, so they need to get attention regarding their nutritional status.

Modeling of poverty level in South Sulawesi Province through spline nonparametric regression approach

This study discussed modeling poverty levels in South Sulawesi through Spline Nonparametric Regression Approach. Since no specific pattern was formed from the relation between the poverty level in South Sulawesi and the factors that influence it, the researchers used nonparametric regression modelling with Spline approach. The Spline model is very good in modelling data that has changeable patterns at certain subintervals. The aimed of this study were to investigate the factors that have the most influence on the poverty level and modelling the poverty level in South Sulawesi through nonparametric regression. The scope of this study was the use of Generalized Cross-Validation (GCV) in selecting optimal knot points; in this case, it used 1, 2, and 3 knots. This study found that three factors mainly influence the poverty level in South Sulawesi in 2017: unemployment, population growth, and literacy rates. The results showed that the best spline model was the model with three-knot points and the minimum GCV value is 11.1155. In addition, the value is 79.75%. It means that the variables of unemployment, population growth, and literacy rate can explain 79.75% of the variation of the poverty variable, while other variables explain the remaining 20.25%.

Bayesian additive regression trees for multivariate skewed responses.

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within-subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub.

Nonparametric Regression Modeling Based on Spline Truncated Estimator on Simulation Data

Regression analysis is one of the statistical analysis used to estimate the pattern of the relationship between predictor variables and response variables . In general, the approach to estimating the regression function is the parametric regression, the nonparametric regression and the semiparametric regression. The approach with parametric regression is used if the shape of the regression curve is assumed to follow a certain pattern such as linear, quadratic, cubic and so on, but in fact there is an unknown pattern of relationship between predictor variables and response variables, so nonparametric regression is used. Then the combination of parametric and nonparametric regression is semiparametric regression. One of the well-known nonparametric regression estimators is the spline truncated. This study was conducted by simulating the relationship pattern of the response variable and the predictor variable that not have specific pattern by following a trigonometric function that formed a regression curve with a standard deviation of 0,05 and 0,25 which formed a different distribution of data, then will be approached with parametric regression (linear, quadratic, cubic) and nonparametric regression (spline truncated linear). Based on the coefficient of determination of each standard deviation, it will shows that the nonparametric regression approach has high flexibility so that it is able to adjust the form of regression curve estimation by itself

A nonparametric-monotone regression model and robust estimation for paired-tow bottom-trawl survey comparative fishing data

Long survey time series tend to produce better stocks assessments; however, over a long time-period it will often be necessary to replace vessels and even survey gears as technology and the objectives of the survey evolve. In this case, survey catchability (Q) may change and comparative fishing experiments are often conducted to provide information about the relative change in Q (i.e. ρ) for the new survey protocol compared to the old one. In this paper we develop a smooth monotone nonparametric regression model for ρ as a function of fish length. Nonparametric regression approaches provide a generic advantage because they can account for a wide range of ρ behavior which is useful when analyzing the many species that are often caught in surveys. However, these models are prone to over-fitting due to sampling variability in data. A monotone nonparametric regression will be less prone to this problem and an improvement, provided the monotone assumptions are warranted. We also apply robust estimation methods to further limit the impact of outliers on estimates of ρ. This includes a simulation analysis to guide specific choices for robust estimation. We apply our methods to three flatfish species and data collected in comparative fishing experiments conducted by Fisheries and Oceans Canada in 1995. We also compare our methods with the approach of Miller (2013) to estimate change in Q. Our main conclusion is that the monotone approach fits about the same as the Miller model but the monotone approach provides more realistic extrapolations and improved precision of ρ at larger lengths that were poorly sampled.

ANALISIS SURVIVAL PENDERITA GAGAL GINJAL DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Survival analysis is a statistical method where the outcome variable that is considered is the time until the occurrence of an event or called an event. Survival analysis in the medical world can be used to test the survival of patients suffering from a disease, one of which is kidney failure. Based on the results of the 2013 Basic Health Research, 0.2% of the total population of Indonesia, namely 499,800 people, experienced kidney failure. Kidney failure is a process of decreasing kidney function that requires renal replacement therapy, in the form of hemodialysis or kidney transplantation. If the survival data involve independent variables that are thought to affect the survival time, then the Cox Proportional Hazard (PH) regression approach can be developed which can be developed for the Multivariate Adaptive Regression Spline (MARS) approach. The MARS approach is a nonparametric regression approach with high-dimensional data, namely data with an independent variable of 3≤𝑝≤20 and a data sample of 50≤N≤1000. This study aims to describe the characteristics of patients with kidney failure, identify the factors that affect the survival time of patients with kidney failure and determine the MARS model. The MARS approach is obtained from a combination of functional basis, maximum interaction, and minimum observation by trial and error. Based on the model that has been formed using the MARS approach, it is found that the most influential variables on the survival time of patients with kidney failure are age, urea levels, and creatine levels.
 Key words: Renal failure, hemodialysis, survival analysis, nonparametric, MARS

Renewable energy and transitioning towards sustainable tourism: Inferences from kernel density and nonparametric approach

This study considered a panel of twenty countries characterized by tourist glut to investigate the role of renewable energy towards sustainable tourism transition. We used a nonparametric kernel density approach to facilitate the analysis and examine the distributional dynamics; besides, using a nonparametric quantile regression approach to trace nexus through different strata. Kernel density estimates reflect multimodality character across the countries. Quantile estimates reveal the negative impact of economic growth on carbon emissions across all quantiles. The coefficient of the international tourist arrival is positive and significant in lower quantiles; nevertheless, it turns out to be negative at higher quantiles implying tourist arrivals reduce carbon emissions. Non-renewable energy consumption positively affects carbon emissions but negatively impacts international tourist arrivals. On the other hand, renewable energy consumption negatively affects carbon emissions but affects the international tourist arrivals positively. This study intends to guide policymakers to hold back fossil fuel dependence for sustainable tourism.

Nonparametric C- and D-vine-based quantile regression

AbstractQuantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic relationship among variables, especially in the tails. We introduce a nonrestrictive and highly flexible nonparametric quantile regression approach based on C- and D-vine copulas. Vine copulas allow for separate modeling of marginal distributions and the dependence structure in the data and can be expressed through a graphical structure consisting of a sequence of linked trees. This way, we obtain a quantile regression model that overcomes typical issues of quantile regression such as quantile crossings or collinearity, the need for transformations and interactions of variables. Our approach incorporates a two-step ahead ordering of variables, by maximizing the conditional log-likelihood of the tree sequence, while taking into account the next two tree levels. We show that the nonparametric conditional quantile estimator is consistent. The performance of the proposed methods is evaluated in both low- and high-dimensional settings using simulated and real-world data. The results support the superior prediction ability of the proposed models.

State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Due to their ease of implementation, equivalent circuit models (ECMs) of batteries are widely used in battery management systems. Generally, ECM parameters vary with operating conditions, thus how such parameter dependencies are addressed substantially influences the accuracy of an ECM over a wide operating range. In this paper, we identify an ECM whose parameters have nonlinear dependence on state-of-charge (SOC). By transforming the SOC-dependent ECM into a linear parameter varying (LPV) input–output model, we propose a non-parametric sparse Gaussian process regression (GPR) approach, which alleviates the difficulty of specifying parametric functional SOC-dependencies of model parameters. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to provide both parameter estimates and their associated uncertainties. The computational cost over large datasets is significantly reduced by adopting the sparse GPR. The proposed approach is applied to the above LPV model with two noise model structures, i.e., white and colored noises. Identification results using experimental data illustrate the efficacy of the proposed approach. The use of colored noise enhances robustness under different noise levels, and achieves higher output prediction accuracy over experimental datasets.

Comparison of simultaneously nonparametric regression based on spline and Fourier series estimator related social aid distribution in Indonesia

Several estimators in the nonparametric regression approach that are often used are the Spline estimator and the Fourier series. Both estimators have high flexibility and can adapt to the local nature of the data effectively. This study applies these two methods to compare the best performance of the formed model. The data used in this study are multi-response data in the form of types of government assistance and multi-predictor data in the form of factors that are thought to affect the distribution of aid receipts in the community. This research is important to optimize the distribution of welfare assistance in Indonesia, especially during the Coronavirus Disease-19 (Covid-19) pandemic. By using the Generalized Cross-Validation (GCV) criteria for the parsimony model, the estimator chosen to predict the distribution of welfare assistance in Indonesia is the Fourier series estimator with cosine and sine bases. The goodness of the nonparametric regression model with the Fourier series estimator can be seen from the minimum GCV value generated with one oscillation parameter, which is 1.483961, with a Mean Square Error (MSE) of 2.1257374, and R 2 of 0.923143. The prediction results are expected to be the government's recommendation for the restoration of community welfare through equitable distribution of aid to support the achievement of the Sustainable Development Goals (SDGs).

Distribution free prediction intervals for multiple functional regression

My research aims to establish a method of constructing prediction intervals for a scalar response of interest when predictors are functional data, with minimal distributional and modeling assumptions. To accommodate flexible regression relationships, we integrated nonparametric functional regression based on functional principal component analysis into our conformal prediction method, and we developed nonparametric functional regression approaches based on the signature method, which is a mathematical tool to represent the information contained in the functions by a collection of iterated integrals. The prediction intervals constructed by the conformal method have guaranteed coverage (confidence) without the heavy restrictions on the error distribution and on the regression function, while the efficiency (implied by the length of the intervals) will depend on the representation and information compression of the functional predictors. Conditions necessary for efficiency and contiguity of the prediction set are discussed. Finally, our methods are illustrated using simulated and real data examples.

Modeling of poverty in Kalimantan with nonparametric spline regression approach

Poverty is a problem that is always present in any country, including Indonesia. Kalimantan is one of the islands in Indonesia that has not been free from poverty. Therefore, more effective efforts to further reduce poverty are needed. The purpose of the is study was to develop a model that can explain significant factors to poverty in Kalimantan. To achive the objective, first, factors that may have significant influence to the changes in the percentage of poor people should be identified, using regression analysis. In this study, nonparametric regression analysis was used with a spline approach since the relationship between poverty and the explanatory factors did not have a particular pattern. The results of this study showed that, the best was using three knot points, where Open Unemployment Rate (OUR), Human Development Index (HDI), and Economic Growth have a significant effect on poverty in Kalimantan.

Tradeoffs between economies of scale and specialization in efficiency for the global semiconductor industry

ABSTRACT This paper combines the semiparametric modified ordinary least-squares approach by Simar et al. (2017) with the nonparametric shape constraint regression approach by Du et al. (2013) to evaluate the performance in the semiconductor industry. Using panel data on 470 companies in the global semiconductor industry over 1999–2018, we compare technical eficiencies between the integrated device manufacturer business model and the fabless-foundry business model. The performance differences between the vertically integrated device manufacturers and the vertically specialized fabless and foundry firms are disentangled by the intensity of labour and capital in a very flexible form. The estimation results indicate that the capital-intensive integrated device manufacturers taking advantage of the economies of scale are operating more efficiently than the niche fabless companies in the global semiconductor industry.

ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE)

Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method. Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained p=(inv(M'M)) M'y

Multi-scale investigation of conditional errors in radar-rainfall estimates

This study examines multi-scale aspects of radar-rainfall (R-R) conditional error, defined as the difference between a given R-R value and the conditional average of corresponding reference observations. To decompose the systematic and random error components, the authors adopted the second-order separation method while previous studies relied on addressing the first-order moment (i.e., conditional bias) only. The authors applied a non-parametric kernel regression approach to characterize the conditional mean and standard deviation and thus to derive a distribution of random component, standardized error. This empirical study is based on data from two rain gauge networks, consisting of 66 and 115 gauges, and two R-R estimates (the Iowa Flood Center and Multi-Radar Multi-Sensor products) over the Iowa domain in the United States. The authors explored the effect of multiple temporal (1–24 h) and spatial (0.5–32 km) scales on the conditional error structure. They found that the error distribution gradually approaches the Gaussian distribution with longer temporal scale while the error feature regarding spatial scale appears to be almost scale-invariant.

System identification and control of heat integrated distillation column using artificial bee colony based support vector regression

Distillation is a high-energy process widely employed in separating fluid mixtures in the oil and gas industries. Heat integration is one of the practical approaches for energy saving in the distillation columns. Proper identification or modeling of heat-integrated distillation column (HIDC) is employed to predict the composition of fluid mixtures. The nonlinear modeling of HIDC is highly challenging, and methods based on the first principles are not effective in coping with the nonlinearities. Hence, a novel, non-parametric support vector regression (SVR) approach is proposed for system identification and control of HIDC in this work. SVR parameters were optimized using artificial bee colony (ABC) algorithm, which resulted in better performance over other meta-heuristic algorithms. Moreover, the SVR model demonstrated better performance than the artificial neural network models in root mean square error (RMSE) and regression coefficient (R). RMSE and R values for ABC-SVR were found to be 0.0010 and 0.99992, respectively, with the validation dataset. The performance of the SVR and PID controllers are also compared. Integral square error (ISE), integral average error (IAE), integral time square error (ITSE), and integral time average error (ITAE) are the comparison metrics employed, which yielded minimal values of and respectively, for the SVR controller. The proposed model outperforms all other related methods, and it can be used to predict the mole fraction of Benzene in Benzene-Toluene HIDC accurately.

Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms

This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya–Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modeling error by 20% and 28% for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.

Re-examining the leverage effect and gold's safe haven properties with the utilization of the implied volatility of gold: a non-parametric quantile regression approach.

Gold as a tradable financial asset has acquired the reputation of a safe haven from market turbulence. The objective of this study is to investigate empirically the relationship between gold prices and implied volatility in the futures markets of gold, re-examine the leverage hypothesis and attempt to make inferences about gold’s safe haven properties. In doing so, it utilizes the recently developed econometric tool of non-parametric quantile regressions. This is the first work to apply the flexible non-parametric quantile regressions on the exchange-traded funds (ETFs) of gold. The data used are daily returns of options of gold shares and implied volatility changes from June of 2008 to December of 2018. The empirical findings indicate that, for the total sample period as well as for almost all of the five sub-periods examined, changes in the implied volatility of gold are insensitive, and not statistically significant, to changes in the price returns of gold. The leverage hypothesis holds for a wide range in the third sub-period. Accordingly, investors in other ETFs (currency or oil) may choose to use gold as shelter during (extreme) economic downturns.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43546-021-00092-3.

Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

World Oil Price Changes and Inflation in Indonesia: A Nonparametric Regression Approach

This study aims to investigate the effect of world oil price changes on inflation in Indonesia. We used a nonparametric regression approach that never been employed in previous studies, both domestically and internationally. This study shows that the second-order Epanechnikov kernel function is statistically significant in explaining the effect of world oil price changes on Indonesia's inflation. We found that the world oil price changes had a lower effect on Indonesia's inflation when its price below USD 100 per barrel, and its effect became higher when its price above USD 100 per barrel. These results have important implications for Bank Indonesia and Indonesia's government in response to the world oil price changes. The policies that aimed at reducing the effect of world oil price changes on inflation in Indonesia should consider the world oil price level.JEL Classification: A12, B26, C14, E31, F29, F62How to Cite:Darmawan, I., Siregar, H., Hakim, D.B., & Manurung, A.H.. (2021). World Oil Price Changes and Inflation in Indonesia: A Nonparametric Regression Approach. Signifikan: Jurnal Ilmu Ekonomi, 10(1), 161-176. https://doi.org/10.15408/sjie.v10i1.19010.