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Estimating True Movement In Time Series As Linear Combination Of Two Indicators

机译:将时间序列中的真实运动估计为两个指标的线性组合

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This paper describes an iterative procedure for estimating true change in a time series as a linear combination of two imperfect indicators with weight chosen to minimize errors particularly when the time series (dependent variables) is not or cannot be observed, this research work was compiled to estimate the coefficients of the ?’s responsiveness of indicators to change in true series, in order to determine the equation that connect variables. It also describes how an appropriate weight could be chosen for the two indicators as to error. We found that; the best estimation for changing the number of caesarean section is; T 1 = 0.1720x + 0.967y 1 - 0.0287 The length of stay in hospital series received a substantial weight compared to the weight of parity series. The final estimate of a x to be positive and reduction of error expected from using the proposed procedure was estimated at about 6% and 27% per month compared to both parity and length of stay in hospital indicators respectively. Key Words: Imperfect Indicators, True Series, Weighted Time series
机译:本文描述了一种估计时间序列中真实变化的迭代过程,该过程是两个不完美指标的线性组合,其权重经选择以最大程度地减少误差,尤其是在没有或无法观察到时间序列(因变量)的情况下。为了确定连接变量的方程式,请估计指标对真实变化的σ响应系数。它还描述了如何为两个指标选择合适的权重以作为误差。我们发现了;改变剖腹产的最佳估计是; T 1 = 0.1720x + 0.967y 1-0.0287与平价系列相比,住院系列的住院时间长了很多。与医院指标的均等性和住院时间相比,使用x射线的最终估计值是正数,并且预期通过使用建议的程序而减少的错误估计分别约为6%和27%。关键词:不完美指标,真实序列,加权时间序列

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