Variance Estimation for Sample Surveys by the Ultimate Cluster Method
Extra variables for domain estimation
Estimation of weighted percentiles
Linearization of the ratio estimator
Linearization of at-risk-of-poverty rate
Linearization of at-risk-of-poverty threshold
Linearization of the aggregate replacement ratio
Linearization of the Gini coefficient I
Linearization of the Gini coefficient II
Linearization of the gender pay (wage) gap.
Linearization of the median income of individuals below the At Risk of...
Linearization of the Quintile Share Ratio
Linearization of the relative median income ratio
Linearization of the relative median at-risk-of-poverty gap
Residual estimation of calibration
The estimation of the simple random sampling.
Variance estimation for measures of annual net change or annual for si...
Variance estimation for measures of change for single and multistage s...
Variance estimation for measures of change for sample surveys for indi...
Variance estimation for measures of annual net change or annual for si...
Variance estimation for cross-sectional, longitudinal measures for sin...
Variance estimation for cross-sectional, longitudinal measures for ind...
Variance estimation of the sample surveys in domain by the ultimate cl...
Variance estimation for sample surveys in domain by the two stratifica...
Variance estimation for sample surveys in domain for one or two stage ...
Variance estimation for sample surveys by the ultimate cluster method
Variance estimation for sample surveys by the new stratification
Estimation of the variance and deff for sample surveys for indicators ...
Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
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