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首页> 外文期刊>International Journal of Statistics and Applications >Estimation of Missing Values for BL (p, 0, p, p) Time Series Models with Student-t Innovations
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Estimation of Missing Values for BL (p, 0, p, p) Time Series Models with Student-t Innovations

机译：带有Student-t创新的BL（p，0，p，p）时间序列模型的缺失值估计

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摘要

In this study optimal linear estimators of missing values for bilinear time series models BL (p, 0, p, p) whose innovations have a student-t distribution are derived by minimizing the h-steps-ahead dispersion error. Data used in the study was simulated using the R Statistical Software where 100 samples of size 500 each were generated for the bilinear model BL (1, 0, 1, 1). The time series data generated was numbered from 1 to 500. In each sample, three data positions 48, 293 and 496 were selected at random and the value at these points removed to create artificial missing values. For comparison purposes, two commonly used non-parametric techniques of artificial neural network (ANN) and exponential smoothing (EXP) estimates were also computed. The performance criteria used to ascertain the efficiency of these estimates were the mean squared error (MSE) and Mean Absolute Deviation (MAD). The study found that ANN estimates were the most efficient for estimating missing values of the bilinear time series with student-t innovations. The study recommends the use of ANN for estimating missing values in bilinear time series model with student errors.
机译：在这项研究中，双线性时间序列模型BL（p，0，p，p）的缺失值的最佳线性估计量是通过最小化h步提前色散误差来推导的，该模型具有创新的t型分布。使用R Statistics软件对研究中使用的数据进行了仿真，其中为双线性模型BL（1、0、1、1）生成了100个大小为500的样本。生成的时间序列数据从1到500编号。在每个样本中，随机选择三个数据位置48、293和496，并删除这些点的值以创建人为的缺失值。为了进行比较，还计算了两种常用的人工神经网络（ANN）和指数平滑（EXP）估计的非参数技术。用于确定这些估计效率的性能标准是均方误差（MSE）和均值绝对偏差（MAD）。这项研究发现，利用学生t创新，ANN估计对于估计双线性时间序列的缺失值最有效。该研究建议使用ANN来估计具有学生错误的双线性时间序列模型中的缺失值。

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