Prevalence and trend estimation from observational data with highly variable post-stratification weights
نویسندگان
چکیده
منابع مشابه
Variance estimation using judgment post-stratification
We consider the problem of estimating the variance of a population using judgment post-stratification. By conditioning on the observed vector of ordered instratum sample sizes, we develop a conditionally unbiased nonparametric estimator that outperforms the sample variance except when the rankings are very poor. This estimator also outperforms the standard unbiased nonparametric variance estima...
متن کاملAdditive Schwarz with Variable Weights
For Additive Schwarz preconditioning of nonsymmetric systems, it is proposed to use weights that change from one iteration to the next. At each iteration, weights for all earlier iterations are implicitly chosen to minimize the current residual. This strategy fits the paradigm of the recently proposed multipreconditioned GMRES. Numerical experiments illustrating the potential of the proposed me...
متن کاملPost - Stratification and Conditional Variance Estimation Richard Valliant
Post-stratification estimation is a technique used in sample surveys to improve efficiency of estimators. Survey weights are adjusted to force the estimated numbers of units in each of a set of estimation cells to be equal to known population totals. The resulting weights are then used in forming estimates of means or totals of variables collected in the survey. For example, in a household surv...
متن کاملGraph labellings with variable weights, a survey
Graph labellings form an important graph theory model for the channel assignment problem. An optimum labelling usually depends on one or more parameters that ensure minimum separations between frequencies assigned to nearby transmitters. The study of spans and of the structure of optimum labellings as functions of such parameters has attracted substantial attention from researchers, leading to ...
متن کاملThe Estimation of Causal Effects from Observational Data
When experimental designs are infeasible, researchers must resort to the use of observational data from surveys, censuses, and administrative records. Because assignment to the independent variables of observational data is usually nonrandom, the challenge of estimating causal effects with observational data can be formidable. In this chapter, we review the large literature produced primarily b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2016
ISSN: 1932-6157
DOI: 10.1214/15-aoas874