VE_Jk_CBS_HT_Total_Hajek: The Campbell-Berger-Skinner unequal probability jackknife...
In samplingVarEst: Sampling Variance Estimation

VE.Jk.CBS.HT.Total.Hajek

R Documentation

The Campbell-Berger-Skinner unequal probability jackknife variance estimator for the Hajek (1971) estimator of a total (Horvitz-Thompson form)

Description

Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the Hajek estimator of a total. It uses the Horvitz-Thompson (1952) variance form.

Usage

VE.Jk.CBS.HT.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N)

Arguments

`VecY.s`	vector of the variable of interest; its length is equal to n, the sample size. Its length has to be the same as that of `VecPk.s`. There must not be missing values.
`VecPk.s`	vector of the first-order inclusion probabilities; its length is equal to n, the sample size. Values in `VecPk.s` must be greater than zero and less than or equal to one. There must not be missing values.
`MatPkl.s`	matrix of the second-order inclusion probabilities; its number of rows and columns equals n, the sample size. Values in `MatPkl.s` must be greater than zero and less than or equal to one. There must not be missing values.
`N`	the population size. It must be an integer or a double-precision scalar with zero-valued fractional part.

Details

For the population total of the variable y:

t = ∑_{k\in U} y_k

the approximately unbiased Hajek (1971) estimator of t is given by:

\hat{t}_{Hajek} = N \frac{∑_{k\in s} w_k y_k}{∑_{k\in s} w_k}

where w_k=1/π_k and π_k denotes the inclusion probability of the k-th element in the sample s. The variance of \hat{t}_{Hajek} can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):

\hat{V}(\hat{t}_{Hajek}) = ∑_{k\in s}∑_{l\in s} \frac{π_{kl}-π_kπ_l}{π_{kl}} \varepsilon_k \varepsilon_l

where

\varepsilon_k = ≤ft(1-\tilde{w}_k\right) ≤ft(\hat{t}_{Hajek}-\hat{t}_{Hajek(k)}\right)

with

\tilde{w}_k = \frac{w_k}{∑_{l\in s} w_l}

and

\hat{t}_{Hajek(k)} = N \frac{∑_{l\in s, l\neq k} w_l y_l}{∑_{l\in s, l\neq k} w_l}

Value

The function returns a value for the estimated variance.

Author(s)

Emilio Lopez Escobar.

References

Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.

Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.

Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.

Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.

Examples

data(oaxaca)                                 #Loads the Oaxaca municipalities dataset
pik.U  <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs.
s      <- oaxaca$sHOMES00                    #Defines the sample to be used
N      <- dim(oaxaca)[1]                     #Defines the population size
y1     <- oaxaca$POP10                       #Defines the variable of interest y1
y2     <- oaxaca$POPMAL10                    #Defines the variable of interest y2
#This approximation is only suitable for large-entropy sampling designs
pikl.s <- Pkl.Hajek.s(pik.U[s==1])           #Approx. 2nd order incl. probs. from s
#Computes the var. est. of the Hajek total point estimator using y1
VE.Jk.CBS.HT.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N)
#Computes the var. est. of the Hajek total point estimator using y2
VE.Jk.CBS.HT.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N)

samplingVarEst documentation built on Jan. 14, 2023, 5:08 p.m.