This study investigates the predictive performance of Concrete Compressive Strength (CCS)
for high-performance, based on concrete mixture constituents and proportioning. A new …

We define extrapolation as any type of statistical inference on a conditional function (eg, a
conditional expectation or conditional quantile) evaluated outside of the support of the …

Derivatives play an important role in bandwidth selection methods (eg, plug-ins), data
analysis and bias-corrected confidence intervals. Therefore, obtaining accurate derivative …

We propose a framework to directly estimate the gradient in multivariate nonparametric
regression models that bypasses fitting the regression function. Specifically, we construct the …

In nonparametric regression, the derivative estimation has attracted much attention in recent
years due to its wide applications. In this paper, we propose a new method for the derivative …

In this paper, we propose a general framework to select tuning parameters for the
nonparametric derivative estimation. The new framework broadens the scope of the …

In this paper, we introduce a new machine learning (ML) model for nonlinear regression
called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of …

Thin film residual stress is proportional to substrate curvature change after film deposition,
based on Stoney's equation. Curvature is approximately equal to the second derivative of …

We propose a nonparametric derivative estimation method for random design without
having to estimate the regression function. The method is based on a variance-reducing …

In the era of big data, there are many data sets recorded in equal intervals of time. To model
the change rate of such data, one often constructs a nonparametric regression model and …