# Trimmed Mean¶

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