Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.     

Simultaneous HPLC quantification of total cholesterol,tocopherols and β-carotene in Barrosã-PDO veal

A simple, rapid and sensitive procedure for the simultaneous determination of total cholesterol, tocopherols and β-carotene in meat is described. The method involves a direct saponification of the meat, a single n-hexane extraction and the analysis of the extracted compounds by normal-phase HPLC, using fluorescence (tocopherols) and UV–Vis photodiode array (cholesterol and β-carotene) detections in tandem. Rates of recovery of spiked meat samples were 93% for cholesterol, 83–86% for (α-, β- and -γ) tocopherols and 89% for β-carotene. Repeatabilities were high (CV < 6%) for all determined compounds, except for δ-tocopherol. This tocopherol, which is not usually present in meat, showed a much lower recovery percentage (73%) and repeatability (12.8%). This methodology was applied for the quantification of total cholesterol, tocopherols and β-carotene in three muscles (longissimus thoracis, longissimus lumborum and semitendinosus) of the Portuguese traditional Barrosã-PDO veal, obtained from autochthonous calves fed extensively during summer (with the least abundant green pastures) and slaughtered in early autumn (October). Barrosã-PDO veal showed median contents of total cholesterol (0.50–0.56 mg/g) and, depending on the analysed muscle, moderate to high contents of α-tocopherol (3.3–3.9 μg/g) and β-carotene (0.07–0.09 μg/g), suggesting an high sensorial and hygienic quality.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Explosive Random‐Coefficient AR(1) Processes and Related Asymptotics for Least‐Squares Estimation

Abstract. Large sample properties of the least‐squares and weighted least‐squares estimates of the autoregressive parameter of the explosive random‐coefficient AR(1) process are discussed. It is shown that, contrary to the standard AR(1) case, the least‐squares estimator is inconsistent whereas the weighted least‐squares estimator is consistent and asymptotically normal even when the error process is not necessarily Gaussian. Conditional asymptotics on the event that a certain limiting random variable is non‐zero is also discussed.     

光学活性物质的工业生产方法

本文介绍了光学８活性牺牲垢几种工业规模的生产方法及最新的进展，分析了各种方法的特点及存在的问题。     

CXFTV2: A Fortran subroutine for the discrete least squares convex approximation

A Fortan subroutine calculates the least squares approximation to n data values containing random errors subject to non-negative second divided differences (convexity). The method employs a dual active set quadratic programming technique that allows several concavities of an iterate to be corrected simultaneously, which is a distinctive feature of this calculation. A B-spline representation of the iterates reduces each active set calculation to an unconstrained minimization with fewer variables that requires only O(n) computer operations. Details in these techniques including the data structure that establishes the implementation of the method are specified. Numerical testing on a variety of data sets indicates that the subroutine is particularly efficient, terminating after a small number of active set changes, the subroutine being suitable for large numbers of data. A numerical example and its output is provided to help the use of the software.     

次生伽马能谱分析方法的实验研究

本文指出一种地化学闭合模型的热中子瞬发活化伽马分析方法，它是地球化学测井的核心技术，标准谱的获得，基于标准谱的加权最小二乘解谱方法、灵敏度刻度及地球化学闭合模型是这一方法的关键环节。     

初至波走时层析成像中的正则化方法

 波场的等效性、观测数据的有限性和观测误差等因素导致地球物理反演本身具有严重的多解性，降低这种多解性的一种重要途径就是对反演过程进行约束。传统的初至波走时层析成像方法，通常是在外部对模型空间施加显式的约束和平滑处理，这样就会破坏模型参数与传播算子之间的相关性，难以达到有效利用先验信息的目的, 甚至会影响反演的稳定性。针对这一问题，本文将先验信息分为三类，在此基础上阐述了通过正则化手段将各种先验信息纳入到反演方程当中，替代传统的外部约束模式。理论试验结果表明，正则化方法比外部约束方法有效地提高了反演的精度和分辨率；将该正则化方法应用于实际二维资料处理，也得到了比较好的反演结果。