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Frey's Scenario F simulation mentioned in account of the Democratic Party's tribulations

U-M Poverty Solutions funds nine projects

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Workshops on EndNote, NIH reporting, and publication altmetrics, Jan 26 through Feb 7, ISR

2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

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Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

Weight Adjustments for the Grouped Jackknife Variance Estimator

Archived Abstract of Former PSC Researcher

Valliant, Richard L., J. Michael Brick, and Jill A. Dever. 2008. "Weight Adjustments for the Grouped Jackknife Variance Estimator." Journal of Official Statistics, 24(3): 469-488.

The jackknife variance estimator is often implemented by dropping groups of units rather than a single unit at a time. This has the practical advantages of economizing on computation time and file size because a separate weight is appended to the analysis file for each jackknife replicate. If the replicate weight adjustments and the grouped jackknife itself are not appropriately constructed, the variance estimates can have some extremely pathological behavior when estimating totals. When the dropout groups do not all have exactly the same number of first-stage units, the standard version of the grouped jackknife may be a severe overestimate. This problem is most likely to arise in single-stage samples with a large number of first-stage units in many of the strata. The standard grouped jackknife variance estimator and two alternatives are examined for the situation of unequally sized groups through a simulation study of school districts in the 50 United States and the District of Columbia.

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