Understanding exponential smoothing via kernel regression

Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.     

Efficient Estimation of an Additive Quantile Regression Model

Abstract. In this paper, two non‐parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel‐based approaches. The second estimator involves sequential fitting by univariate local polynomial quantile regressions for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The purpose of the extra local averaging is to reduce the variance of the first estimator. We show that the second estimator achieves oracle efficiency in the sense that each estimated additive component has the same variance as in the case when all other additive components were known. Asymptotic properties are derived for both estimators under dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative empirical application of additive quantile models to ambulance travel times.     

W9Cr4V高速钢非光滑表面耐磨性正交设计试验研究

采用MG-2000高速高温摩擦磨损试验机,用正交设计试验方法研究凹坑形非光滑表面形态的耐磨性,选取温度、凹坑直径和凹坑间距作为试验因素,每个因素选取2个水平。试验结果表明,在本次试验条件下,温度对非光滑试样耐磨性影响最大,凹坑直径其次,凹坑间距最次,这为凹坑型非光滑表面尺寸和分布密度的选取提供了初步的试验数据。     

Estimation and inference for generalized semi-varying coefficient models

This paper is concerned with the estimation and inference in generalized semi-varying coefficient models. An orthogonal projection local quasi-likelihood estimation is investigated, which can easily be used to estimate the model parametric and nonparametric parts. Then an empirical likelihood logarithmic approach to construct the confidence regions/intervals of the nonparametric parts is developed. Under some mild conditions, the asymptotic properties of the resulting estimators are studied explicitly, respectively. Some simulation studies are carried out to examine the finite sample performance of the proposed methods. Finally, the methodologies are illustrated by a real data set.     

Adaptive lasso for accelerated hazards models

The important feature of the accelerated hazards (AH) model is that it can capture the gradual effect of treatment. Because of the complexity in its estimation, few discussion has been made on the variable selection of the AH model. The Bayesian non-parametric prior, called the transformed Bernstein polynomial prior, is employed for simultaneously robust estimation and variable selection in sparse AH models. We first introduce a naive lasso-type accelerated hazards model, and later, in order to reduce estimation bias and improve variable selection accuracy, we further consider an adaptive lasso AH model as a direct extension of the naive lasso-type model. Through our simulation studies, we obtain that the adaptive lasso AH model performs better than the lasso-type model with respect to the variable selection and prediction accuracy. We also illustrate the performance of the proposed methods via a brain tumour study.     

Construction and Nonparametric Testing of Orthogonal Arrays through Algebraic Strata and Inequivalent Permutation Matrices

In this article we use a new methodology, based on algebraic strata, to generate the class of all the orthogonal arrays of given size and strength. From this class we extract all the non isomorphic orthogonal arrays. Then, using all these non isomorphic orthogonal arrays, we suggest a method based on the inequivalent matrices permutations testing procedures Basso et al. (2004 Basso , D. , Evangelaras , H. , Koukouvinos , C. , Salmaso , L. ( 2004 ). Nonparametric testing for main effects on inequivalent designs. Proc. 7th Int. Workshop Model-Oriented Design Anal. Heeze, Netherlands, June 14–18 . [Google Scholar]) in order to obtain separate permutation tests for the effects in unreplicated mixed level fractional factorial designs. In order to validate the proposed method we perform a Monte Carlo simulation study and find out that the permutation tests appear to be a valid solution for testing effects, in particular when the usual normality assumptions cannot be justified.     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.     

落葵果实中总黄酮提取条件的研究

采用超声波提取法,通过正交实验确定从落葵果实中提取总黄酮的最佳工艺条件,并用比色法测定提取物中总黄酮的含量.结果表明,最佳条件是以体积分数为70%的乙醇为提取剂、料液比为1：20、提取时间为35min.在此条件下,落葵果实总黄酮提取量为5.3 mg蛐g.     

石家庄六线隧道模型试验相似材料研究

满足相似关系的模型材料是模型试验成功的关键。在调研相似材料研究现状的基础上,依托石家庄六线隧道模型试验项目,应用量纲分析法推导出了相关参数的相似判据。根据相似原则,以粘聚力和摩擦角为主要相似判据,选用重晶石粉、石英砂和凡士林的混合物为模型材料,通过正交设计法研制出了地层相似材料。以抗压强度为相似判据,选用石膏材料为模型材料,研制出了灌注桩和锚索注浆体相似材料。