Nonparametric regression is commonly used for summarizing the relationship between variables without requiring the assumptions of model. Generalized linear model and linear regression model are usually used to examine the relationship of variables, but both are badly affected by influential observations. Due to this, detection and removal of outliers attain a lot of attention of researchers to obtain reliable estimates. We focus on such robust technique whose performance is acceptable in the presence of outliers. The present article empirically compared the performance of linear regression model and generalized linear model with multivariate nonparametric kernel regression. Here, multivariate nonparametric kernel regression is used with Gaussian kernel and six different bandwidths on Aerial biomass data. The performance of nonparametric regression with Bayesian bandwidth was found to be better as compared with other methods.