Combining Estimates from Multiple Surveys Combining estimates from multiple surveys can be very useful, especially when the question of interest cannot be addressed well by a single, existing survey In this paper, we provide a brief review of methodology for combining estimates, with a focus on dual frame, weighting-based, joint-modeling, missing-data, and small-area methods
Combining Survey Data with Other Data Sources - Project Euclid been proposed, and describe research that is needed for combining survey estimates Key words and phrases: Hierarchical models, imputation, multiple frame survey, probability sample, record linkage, small area estimation 1 INTRODUCTION How can we collect data that give accurate and timely estimates of quantities of interest, and assess
Statistical Methods for Combining Multiple Data Sources In the panel's first report, we described the multiple types of additional data sources—federal and state administrative data, electronic health records, web scrapings, credit card transactions, satellite images, and sensor data, among others—that might be used to improve the level of detail, timeliness, and cost of federal statistics Federal statistical agencies have long used
mathematical statistics - Can I merge multiple linear . . . So yes, you could use the average of the multiple regressions With some more information one could tell if there are smarter ways to combine the figures E g when the data is not the same then the estimates can have different precision and for that case you could use a weighted estimate in which you do not count every result the same
Combining estimators of a common parameter across samples 4 Examples where information additivity fails It remains to clarify that the asymptotic optimality of weighted linear combinations of separate-sample estimators in the combined sample cannot persist generally when the nuisance parameters in the two samples are coupled, i e , partially shared or related through common constraints
Section 3. Combining samples - Statistics Canada As the following example demonstrates, the ratio of the variance estimators can even have zero variance Thus it can sometimes provide the optimal weighting even if the variances are unknown Example 5: Assume we want to combine estimates resulting from two simple random samples of different sizes This can of course be done optimally without