Consider the following nonparametric model: \(Y_{ni}=g(x_{ni})+ \varepsilon _{ni},1\le i\le n,\) where \(x_{ni}\in {\mathbb {A}}\) are the nonrandom design points and \({\mathbb {A}}\) is a compact set of \({\mathbb {R}}^{m}\) for some \(m\ge 1\), \(g(\cdot )\) is a real valued function defined on \({\mathbb {A}}\), and \(\varepsilon _{n1},\ldots ,\varepsilon _{nn}\) are \(\rho ^{-}\)-mixing random errors with zero mean and finite variance. We obtain the Berry–Esseen bounds of the weighted estimator of \(g(\cdot )\). The rate can achieve nearly \(O(n^{-1/4})\) when the moment condition is appropriate. Moreover, we carry out some simulations to verify the validity of our results.
To establish a new method of testing and evaluating the quality of refined montan wax (RMW), digital color and GC fingerprint technology were introduced and applied. CIE Lab color mode was used to digitize the exterior colors of RMW, and the score obtained through a fitting function was also used to reflect its quality. It is shown that they were in complete accord with the human visual perception trend. The GC fingerprint was used to characterize the internal chemical information of RMW, and the composition of its internal features was reflected through the relative retention time (RRT) and relative peak area (RPA) values. It is shown that there was a high degree of similarity between the fingerprints, while certain differences also existed. This can be used to implement effective application of RMW to aspects such as quality control, adulteration identification, and origin attributions.
