Search results
Results from the WOW.Com Content Network
The weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points contributing equally to the final average, some data points contribute more than others.
The result of this application of a weight function is a weighted sum or weighted average. Weight functions occur frequently in statistics and analysis , and are closely related to the concept of a measure .
The idea of the kernel average smoother is the following. For each data point X 0 , choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than λ {\displaystyle \lambda } to X 0 (the closer to X 0 points get higher weights).
In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance (i.e., proportional to its precision). Given a sequence of independent observations y i with variances σ i 2, the inverse ...
In statistics, the weighted geometric mean is a generalization of the geometric mean using the weighted arithmetic mean.. Given a sample = (, …,) and weights = (,, …,), it is calculated as: [1]
EWMA weights samples in geometrically decreasing order so that the most recent samples are weighted most highly while the most distant samples contribute very little. [ 2 ] : 406 Although the normal distribution is the basis of the EWMA chart, the chart is also relatively robust in the face of non-normally distributed quality characteristics.
Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. [1]
The resulting point estimate is therefore like a weighted average of the sample mean ¯ and the prior mean =. This turns out to be a general feature of empirical Bayes; the point estimates for the prior (i.e. mean) will look like a weighted averages of the sample estimate and the prior estimate (likewise for estimates of the variance).