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Titlebook: Introduction to Variance Estimation; Kirk M. Wolter Book 2007 Springer-Verlag New York 2007 Estimator.Evaluation.Excel.Logistic Regression

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發(fā)表于 2025-3-23 12:50:53 | 只看該作者
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發(fā)表于 2025-3-23 17:02:30 | 只看該作者
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發(fā)表于 2025-3-23 23:58:35 | 只看該作者
Introduction,private sector, the academic community, and various governmental agencies, both in the United States and abroad. Examples include market research and public opinion surveys; surveys associated with academic research studies; and large nationwide surveys about labor force participation, health care,
15#
發(fā)表于 2025-3-24 04:27:18 | 只看該作者
The Method of Random Groups,r each sample; constructing a separate estimate of the population parameter of interest from each sample and an estimate from the combination of all samples; and computing the sample variance among the several estimates. Historically, this was one of the first techniques developed to simplify varian
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發(fā)表于 2025-3-24 07:35:10 | 只看該作者
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發(fā)表于 2025-3-24 14:15:23 | 只看該作者
The Jackknife Method,f the class of variance estimators that employ the ideas of subsample replication. Another subsample replication technique, called the jackknife, has also been suggested as a broadly useful method of variance estimation. As in the case of the two previous methods, the jackknife derives estimates of
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發(fā)表于 2025-3-24 18:35:18 | 只看該作者
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發(fā)表于 2025-3-24 19:37:50 | 只看該作者
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發(fā)表于 2025-3-25 01:36:48 | 只看該作者
Generalized Variance Functions,o the expectation of the estimator. If the parameters of the model can be estimated from past data or from a small subset of the survey items, then variance estimates can be produced for all survey items simply by evaluating the model at the survey estimates rather than by direct computation. We sha
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