INCORPORATING DESIGN WEIGHTS AND HISTORICAL DATA INTO MODEL-BASED SMALL-AREA ESTIMATION
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

INCORPORATING DESIGN WEIGHTS AND HISTORICAL DATA INTO MODEL-BASED SMALL-AREA ESTIMATION

Filetype[PDF-998.02 KB]


  • English
    • Details Supporting Files You May Also Like

    Details:

    • Alternative Title:
      J Data Sci
    • Personal Author:
    • Description:
      Bayesian hierarchical regression (BHR) is often used in small area estimation (SAE). BHR conditions on the samples. Therefore, when data are from a complex sample survey, neither survey sampling design nor survey weights are used. This can introduce bias and/or cause large variance. Further, if non-informative priors are used, BHR often requires the combination of multiple years of data to produce sample sizes that yield adequate precision; this can result in poor timeliness and can obscure trends. To address bias and variance, we propose a design assisted model-based approach for SAE by integrating adjusted sample weights. To address timeliness, we use historical data to define informative priors (power prior); this allows estimates to be derived from a single year of data. Using American Community Survey data for validation, we applied the proposed method to Behavioral Risk Factor Surveillance System data. We estimated the prevalence of disability for all U.S. counties. We show that our method can produce estimates that are both more timely than those arising from widely-used alternatives and are closer to ACS' direct estimates, particularly for low-data counties. Our method can be generalized to estimate the county-level prevalence of other health related measurements.
    • Subjects:
    • Source:
      J Data Sci. 18(1):115-131
    • Pubmed ID:
      32336972
    • Pubmed Central ID:
      PMC7182002
    • Document Type:
      Journal Article
    • Funding:
      CC999999/ImCDC/Intramural CDC HHSUnited States/
    • Collection(s):
    • Main Document Checksum:
    • Download URL:
    • File Type:

    You May Also Like

    Checkout today's featured content at stacks.cdc.gov