VE.Jk.Tukey.Corr.NHT {samplingVarEst}

The Tukey (1958) jackknife variance estimator for the estimator of a correlation coefficient using the Narain-Horvitz-Thompson point estimator

Description

Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Narain (1951); Horvitz-Thompson (1952) point estimator.

Usage

VE.Jk.Tukey.Corr.NHT(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)

Arguments

VecY.s

vector of the variable of interest Y; 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 variable of interest X; 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.

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.

N

the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is also utilised for the finite population correction; see FPC below.

FPC

logical value. If an ad hoc finite population correction FPC=1-n/N is to be used. The default is TRUE.

Details

For the population correlation coefficient of two variables y and x:

C = \frac{\sum_{k\in U} (y_k - \bar{y})(x_k - \bar{x})}{\sqrt{\sum_{k\in U} (y_k - \bar{y})^2}\sqrt{\sum_{k\in U} (x_k - \bar{x})^2}}

the point estimator of C is given by:

\hat{C} = \frac{\sum_{k\in s} w_k (y_k - \hat{\bar{y}}_{NHT})(x_k - \hat{\bar{x}}_{NHT})}{\sqrt{\sum_{k\in s} w_k (y_k - \hat{\bar{y}}_{NHT})^2}\sqrt{\sum_{k\in s} w_k (x_k - \hat{\bar{x}}_{NHT})^2}}

where \hat{\bar{y}}_{NHT} is the Narain (1951); Horvitz-Thompson (1952) estimator for the population mean \bar{y} = N^{-1} \sum_{k\in U} y_k,

\hat{\bar{y}}_{NHT} = \frac{1}{N}\sum_{k\in s} w_k y_k

and w_k=1/\pi_k with \pi_k denoting the inclusion probability of the k-th element in the sample s. The variance of \hat{C} can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):

\hat{V}(\hat{C}) = \left(1-\frac{n}{N}\right)\frac{n-1}{n}\sum_{k\in s} \left( \hat{C}_{(k)}-\hat{C} \right)^2

where \hat{C}_{(k)} has the same functional form as \hat{C} but omitting the k-th element from the sample s. We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction 1-n/N (see Shao and Tu, 1995; Wolter, 2007). If FPC=FALSE, then the term 1-n/N is omitted from the above formula.

Value

The function returns a value for the estimated variance.

Author(s)

Emilio Lopez Escobar.

References

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.

Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.

Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.

Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.

Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.

Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.

See Also

Est.Corr.Hajek

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
x     <- oaxaca$HOMES10                     #Defines the variable of interest x
#Computes the var. est. of the corr. coeff. point estimator using y1
VE.Jk.Tukey.Corr.NHT(y1[s==1], x[s==1], pik.U[s==1], N)
#Computes the var. est. of the corr. coeff. point estimator using y2
VE.Jk.Tukey.Corr.NHT(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)