Application Of Nonparametric Approach Research Articles

Estimating sustainable transport efficiency and socioeconomic factors: application of non-parametric approach

ABSTRACT Sustainable transport has received substantial attention globally. Evidently, transportation is a major contributor to global carbon emission, which causes the degradation of environmental quality and safety. Economic-oriented activities are performed in terms of inclination aspect. Thus, this paper aims to analyze whether and how environmental pollution and fatality, economic-oriented transport, and socioeconomic factors affect transport efficiency. For this purpose, panel data from 2000 to 2020 for 35 OECD countries are used. First, data envelopment analysis slack-based measure and windows analysis are employed to measure transport efficiency by using fourteen inputs/output variables. Second, econometric analysis demonstrates the relationship between socioeconomic factors and sustainable transport efficiency. Outcomes suggest that 30.2% increase in sustainable transport efficiency is due to a 1% increase in transport–household expenditures. OECD countries significantly performed with high economic efficiency scores for the examined period. Econometric analysis indicates that socioeconomic factors have a remarkable impact on sustainable transport efficiency.

Untangling crop management and environmental influences on wheat yield variability in Bangladesh: An application of non-parametric approaches

In South Asia, wheat is typically grown in favorable environments, although policies promoting intensification in Bangladesh's stress-prone coastal zone have resulted in expanded cultivation in this non-traditional area. Relatively little is known about how to best manage wheat in these unique environments. Research is thus needed to identify ‘best-bet’ entry points to optimize productivity, but classical parametric analyses offer limited applicability to elucidate the relative importance of the multiple factors and interactions that influence yield under such conditions. This problem is most evident in datasets derived from farmer-participatory research, where missing values and skewed data are common. This paper examines the predictive power of three non-parametric approaches, including linear mixed effects models (LMMs), and two binary recursive partitioning methods: classification and regression trees (CARTs) and Random Forests. We collected yield, crop management, and environmental observations from 422 wheat fields in the 2012–13 season, across six production environments spanning southern Bangladesh, where nutrient rates and genotypes were imposed, but management of other production factors varied from farmer to farmer. Fields were grouped into categories including early- and late-sowing, depending on crop establishment before or after December 15, respectively, and in combination, across both early- and late-sowing groups. For each of these groups, we investigated how each non-parametric analysis predicted the factors influencing yield. All three approaches identified nitrogen rate and environment as the most important factors, regardless of sowing category. CART also identified assemblages of high- and low-yielding environments, although those located in saline and warmer thermal zones were not necessarily the lowest yielding, indicating that farmers can optimize crop management to overcome these constraints. The number of days farmers sowed wheat before or after December 15, days to maturity, and the number of irrigations and weedings also influenced yield, though each method weighted these factors differently. LMMs also indicated a slight yield advantage when farmers used stress-tolerant genotypes, though CART and Random Forests did not. One-to-one plots for observed vs. predicted yields from LMMs and Random Forests showed better performance by the former than the latter, with smaller root mean square and mean absolute error for the combined, early- and late-sowing groups, respectively. While the LMMs were superior in this case, Random Forests may still prove useful in the classification and interpretation of farm survey data in which no treatment interventions have been administered.

Modeling Magnetorheological Dampers with Application of Nonparametric Approach

This article studies the application of nonparametric modeling approach to model magnetorheological (MR) dampers. For comparison purposes, another typical parametric modeling method for electrorheological (ER) and MR dampers is reviewed. The existing parametric MR damper model includes a stiff Bouc-Wen model that is not friendly for simulation study and real time implementation of model-based advanced control algorithms. In order to avoid the difficulties by using the existing parametric model, the test data from a commercialized MR damper is employed to develop nonparametric models, which can consist of a series of numerically efficient mathematic functions. In addition, the selected functions are required to be continuous and differentiable for potential model-based control algorithms. The results of the nonparametric models show that such different models are comparable. Furthermore, one nonparametric model is selected to be compared with a parametric model and the test data to illustrate the accuracy of the model. The comparison shows that the proposed nonparametric models are able to accurately predict the damper force characteristics, damper bilinear behavior, hysteresis, and electromagnetic saturation. It is further shown that the nonparametric models can be numerically solved with an integration step size of the order of 10 2 s, much faster than the parametric models of the order of 10 5 s, which clearly shows that the proposed nonparametric models are feasible even for real time model-based control algorithms.