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  1. Prediction interval. In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis .

  2. 2017年12月11日 · 置信区间是对参数的估计。. 参数一般是一个固定的常数,而预测区间是对随机变量的估计。. 估计的过程本身就会有估计误差,但是如果估计随机变量,那就在估计误差的基础上还要加上随机变量本身的标准差,所以预测区间自然就比置信区间要宽一些。. 知乎 ...

  3. 2021年8月31日 · Prediction intervals represent a range of values that are likely to contain the true value of some response variable for a single new observation based on specific values of one or more predictor variables. For example, suppose we fit a simple linear regression model that uses the number of bedrooms to predict the selling price of a house:

  4. 基准方法. 对于四种基准方法,在残差不相关的前提下,通过数学推导得出预测标准差是可能的。 如果令 ^σh σ ^ h 表示h步预测的标准差,并令 ^σ σ ^ 为残差标准差,那么我们可以用下面的表达式: 均值预测 a_h = $ Naïve 预测: ^σh = ^σ√h σ ^ h = σ ^ h. 季节性naïve预测 ^σh = ^σ√⌊(h −1)/m⌋+1 σ ^ h = σ ^ ⌊ ( h − 1) / m ⌋ + 1. 漂移预测: ^σh = ^σ√h(1+h/T) σ ^ h = σ ^ h ( 1 + h / T). 注意,当 h = 1 h = 1 且 T T 很大时,这些方法都可以计算出相同的近似值 ^σ σ ^ 。 不论使用何种基准预测方法,都能计算出预测区间。

  5. A prediction interval is a type of confidence interval (CI) used with predictions in regression analysis; it is a range of values that predicts the value of a new observation, based on your existing model. Prediction and confidence intervals are often confused with each other. However, they are not quite the same thing.

  6. 2021年2月16日 · Tutorial Overview. This tutorial is divided into 5 parts; they are: What Is Wrong With a Point Estimate? What Is a Prediction Interval? How to Calculate a Prediction Interval. Prediction Interval for Linear Regression. Worked Example. Need help with Statistics for Machine Learning? Take my free 7-day email crash course now (with sample code).

  7. In this section, we are concerned with the prediction interval for a new response, \(y_{new}\), when the predictor's value is \(x_h\). Again, let's just jump right in and learn the formula for the prediction interval. The general formula in words is as always: t-multiplier ×

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