VE.Jk.CBS.HT.Ratio {samplingVarEst}

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

Description

Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of a ratio of two totals/means. It uses the Horvitz-Thompson (1952) variance form.

Usage

VE.Jk.CBS.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)

Arguments

VecY.s

vector of the numerator variable of interest; its length is equal to n, the sample size. Its length has to be the same as that of VecPk.s and VecX.s. There must not be missing values.

VecX.s

vector of the denominator variable of interest; its length is equal to n, the sample size. Its length has to be the same as that of VecPk.s and VecY.s. There must not be missing values. All values of VecX.s should be greater than zero. A warning is displayed if this does not hold, and computations continue if mathematical expressions allow this kind of values for the denominator variable.

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.

Details

For the population ratio of two totals/means of the variables y and x:

R = \frac{\sum_{k\in U} y_k/N}{\sum_{k\in U} x_k/N} = \frac{\sum_{k\in U} y_k}{\sum_{k\in U} x_k}

the ratio estimator of R is given by:

\hat{R} = \frac{\sum_{k\in s} w_k y_k}{\sum_{k\in s} w_k x_k}

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

\hat{V}(\hat{R}) = \sum_{k\in s}\sum_{l\in s} \frac{\pi_{kl}-\pi_k\pi_l}{\pi_{kl}} \varepsilon_k \varepsilon_l

where

\varepsilon_k = \left(1-\tilde{w}_k\right) \left(\hat{R}-\hat{R}_{(k)}\right)

with

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

and

\hat{R}_{(k)} = \frac{\sum_{l\in s, l\neq k} w_l y_l/\sum_{l\in s, l\neq k} w_l}{\sum_{l\in s, l\neq k} w_l x_l/\sum_{l\in s, l\neq k} w_l} = \frac{\sum_{l\in s, l\neq k} w_l y_l}{\sum_{l\in s, l\neq k} w_l x_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.

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.

See Also

VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio

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
y1     <- oaxaca$POP10                       #Defines the numerator variable y1
y2     <- oaxaca$POPMAL10                    #Defines the numerator variable y2
x      <- oaxaca$HOMES10                     #Defines the denominator variable x
#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 ratio point estimator using y1
VE.Jk.CBS.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s)
#Computes the var. est. of the ratio point estimator using y2
VE.Jk.CBS.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)