光行差对恒星时的影响分析

光行差是观测者的运动速度与星光的运动速度相结合(相对运动)产生的一种光学效应。各种光行差对恒星的观测均会产生影响,本文阐述了周日光行差和周年光行差对恒星坐标(主要是赤经)的影响,并进一步分析光行差对地方恒星时产生的影响。     

时标上二阶中立型时滞动力方程的振动性

借助时间尺度的有关理论，运用 Riccati 变换技巧，给出时间尺度上二阶非线性中立型时滞动力方程的新的振动准则。推广并改进时标上二阶非线性时滞动力方程振动性的一些结果。     

Forecasting time series with increasing seasonal variation

Four options for modeling and forecasting time series data containing increasing seasonal variation are discussed, including data transformations, double seasonal difference models and two kinds of transfer function-type ARIMA models employing seasonal dummy variables. An explanation is given for the typical ARIMA model identification analysis failing to identify double seasonal difference models for this kind of data. A logical process of selecting one option for a particular case is outlined, focusing on issues of linear versus non-linear increasing seasonal variation, and the level of stochastic versus deterministic behavior in a time series. Example models for the various options are presented for six time series, with point forecast and interval forecast comparisons. Interval forecasts from data-transformation models are found to generally be too wide and sometimes illogical in the dependence of their width on the point forecast level. Suspicion that maximum likelihood estimation of ARIMA models leads to excessive indications of unit roots in seasonal moving-average operators is reported.     

潘二矿煤系高岭岩脱除Fe_2O_3工艺的研究

本文通过对潘二矿煤系高岭岩脱杂工艺实验，认为较佳脱杂效果的工艺条件为焙烧温度430℃，浸出温度120℃，盐酸浓度20％，浸出时间120min。     

中文科技期刊线性增长模型

在描述文献增长的诸多模型中，线性增长模型更适合于某一特定文献类型的短期预测。本文依据我国７８至９６年各年间中文科技期刊的种数，利用最小平方法，拟合出中文科技期刊种数随时间增长的线性方程。针对各年间科技期刊种数存在着自相关的现象，建立了剔除序列相关后的预测模型。     

一种新的不稳定导热有限差分格式

本文提出不稳定导热问题的一种新的有限差分格式。这种格式根据所取时间、空间步长及边界条件确定相应的温度加权因子。这样给出的差分格式与C-N格式计算量相同,但准确性却明显提高,并且是无条件稳定的。     

关于一元线性回归简化计算公式的注记

本文用初等变换方法，给出了一元线性回归预测公式ｙ∧＝ａ＋ｂｘ中参数ａ和ｂ的简化计算公式．     

一类矩阵方根的直接表达式

本文对特征值在复平面上任意一边界线过原点的半平面上的矩阵,给出级数形式的方根公式,这种公式用矩阵本身直接表达其方根。作为特例,我们得到了正定矩阵的正定 p 次方根的直接求法。     

Recursive estimation and forecasting of non-stationary time series

The paper presents a unified, fully recursive approach to the modelling and forecasting of non-stationary time-series. The basic time-series model, which is based on the well-known ‘component’ or ‘structuraL’ form, is formulated in state-space terms. A novel spectral decomposition procedure, based on the exploitation of recursive smoothing algorithms, is then utilized to simplify the procedures of model identification and estimation. Finally, the fully recursive formulation allows for conventional or self-adaptive implementation of state-space forecasting and seasonal adjustment. Although the paper is restricted to the consideration of univariate time series, the basic approach can be extended to handle explanatory variables or full multivariable (vector) series.