Trimmed MeanΒΆ

Trimmed Mean algorithm chops off values at the high and low extremes and applies the Mean algorithm to the remaining observations. In this way, the effect caused by a small number of extreme values can be offsetted.

Input Parameters

Parameter Type Constraint Description Remarks
\(Y\) \(Y \in \mathbb R^N\) \(N \in \mathbb{N}\) Input data vector of length \(N\) None
\(p\) \(p \in \mathbb R\) \(0\% \lt p \lt 50\%\) Percentage of high and low extremes to be trimmed None

Output Parameters

Parameter Type Constraint Description Remarks
\(\mu\) \(\mu \in \mathbb R\) None Trimmed mean value of data \(Y\) The result is sensitive to improper values such as ‘nan’, ‘inf’, ‘null’, etc.

Single Steps using the Algorithm