pooled.Estimator {rQCC}

Pooled Estimator

Description

This function calculates the pooled estimator based on the unbiased estimators such as the mean, median, Hodges-Lehmann (HL1, HL2, HL3), standard deviation, range, median absolute deviation (MAD) and Shamos estimators.

Usage

pooledEstimator(x, estimator = c("mean", "median", "HL1", "HL2", "HL3", 
                                 "sd", "range", "mad", "shamos"), 
                   poolType=c("A", "B", "C") )

Arguments

x

a numeric list of observations.

estimator

a character string specifying the estimator, must be one of "mean" (default), "median", "HL1", "HL2", "HL3", "sd", "range", "mad", and "shamos".

poolType

Type for how to pool estimators, must be one of "A" (default), "B", and "C".

Details

This function calculates the pooled estimator based on one of "mean" (default), "median", "HL1", "HL2", "HL3", "sd", "mad", and "shamos", which are all unbiased. There are three different methods of pooling the estimators, denoted by "A" (default), "B", and "C". For more details on how to pool them, refer to vignette.

Value

They return a numeric value.

Author(s)

Chanseok Park

References

Park, C. and M. Wang (2020). A study on the X-bar and S control charts with unequal sample sizes. Mathematics, 8(5), 698.
doi: 10.3390/math8050698

Park, C., H. Kim, and M. Wang (2022). Investigation of finite-sample properties of robust location and scale estimators. Communications in Statistics - Simulation and Computation, 51, 2619-2645.
doi: 10.1080/03610918.2019.1699114

Examples

x1 = c(1,2,3,4,5)
x2 = c(6,7)
x  = list(x1,x2)

# Pooled sample mean (default) by type "A" pooling
pooledEstimator(x) 
pooledEstimator(x, "mean", "A") # same as the above 

# Pooled sample mean by type "B" pooling
pooledEstimator(x, "mean", "B")


# Pooled sample sd by type "B" pooling
pooledEstimator(x, estimator="sd", pool="B")