GENERALIZED VARIANCE FUNCTIONS FOR A COMPLEX SAMPLE SURVEY

1987 ◽  
Vol 1987 (1) ◽  
pp. i-36
Author(s):  
Eugene G. Johnson ◽  
Benjamin F. King
2014 ◽  
Vol 30 (1) ◽  
pp. 63-90 ◽  
Author(s):  
MoonJung Cho ◽  
John L. Eltinge ◽  
Julie Gershunskaya ◽  
Larry Huff

Abstract Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.


Test ◽  
2014 ◽  
Vol 23 (3) ◽  
pp. 585-606 ◽  
Author(s):  
Yacouba Boubacar Maïnassara ◽  
Célestin C. Kokonendji

2021 ◽  
Vol 12 (1) ◽  
pp. 77-87
Author(s):  
Nuraini Nuraini ◽  
Amrina Rosyada

The number of people with rheumatism worldwide has reached 355 million, and this is estimated by 2025, suggesting that more than 25% will experience paralysis. This study aims to determine obesity and other factors related to the increased risk of rheumatic diseases in Indonesia, the method used was data analysis using a complex sample survey. It used 2014 IFLS data and a cross sectional study design, as well as a multistage random sampling with a total of 29,106 respondents, and the results showed that the prevalence of rheumatic disease in Indonesia was 5.2% in 2014. The most dominant and unmodifiable variable that influenced incidence was gender (PR=1.686; 95% CI=1.488-1.910). Meanwhile, obesity is the most dominant and modifying variable that influences the incidence of rheumatic disease (PR=1.630; 95% CI=1.433-1.855). Factors that are simultaneously related to the increased risk of rheumatic diseases include age, gender, education, physical activity, protein consumption, obesity, and accident history. Considering the results, patients need to eat healthy and low purine foods, as well as implementing other healthy lifestyles such as appropriate, adequate, and regular physical activities in order to reduce the risk of rheumatism.


Author(s):  
Phillip S. Kott

Coverage intervals for a parameter estimate computed using complex survey data are often constructed by assuming the parameter estimate has an asymptotically normal distribution and the measure of the estimator’s variance is roughly chi-squared. The size of the sample and the nature of the parameter being estimated render this conventional “Wald” methodology dubious in many applications. I developed a revised method of coverage-interval construction that “speeds up the asymptotics” by incorporating an estimated measure of skewness. I discuss how skewness-adjusted intervals can be computed for ratios, differences between domain means, and regression coefficients.


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