M3pow2one: Unbiased central moment estimates

In innager/edgee: Edgeworth Expansions and Higher-Order Inference

Unbiased central moment estimates

Description

Calculate unbiased estimates of central moments and their powers and products.

Usage

M3pow2one(m2, m3, m4, m6, n)

Arguments

m2	naive biased variance estimate m_2 = 1/n \sum_{i = 1}^n ((X_i - \bar{X})^2 for a vector X.
m3	naive biased third central moment estimate m_3 = 1/n \sum_{i = 1}^n ((X_i - \bar{X})^3 for a vector X.
m4	naive biased fourth central moment estimate m_4 = 1/n \sum_{i = 1}^n ((X_i - \bar{X})^4 for a vector X.
m6	naive biased sixth central moment estimate m_6 = 1/n \sum_{i = 1}^n ((X_i - \bar{X})^6 for a vector X.
n	sample size.

Value

Unbiased estimate of squared third central moment \mu_3^2, where \mu_3 is a third central moment.

See Also

Other unbiased estimates: M2M3one(), M2M3two(), M2M4one(), M2M4two(), M2one(), M2pow2one(), M2pow2two(), M2pow3one(), M2pow3two(), M2two(), M3one(), M3pow2two(), M3two(), M4one(), M4two(), M5one(), M5two(), M6one(), M6two()

Examples

    
n <- 10
smp <- rgamma(n, shape = 3)
for (j in 2:6) {
  assign(paste("m", j, sep = ""), mean((smp - mean(smp))^j))
}
M3pow2one(m2, m3, m4, m6, n)    

innager/edgee documentation built on April 24, 2024, 8:14 p.m.