Download e-book for iPad: Introduction to Variance Estimation (Statistics for Social by Kirk Wolter

By Kirk Wolter

ISBN-10: 038732917X

ISBN-13: 9780387329178

ISBN-10: 0387350993

ISBN-13: 9780387350998

Now to be had in paperback, this e-book is equipped in a manner that emphasizes either the speculation and purposes of a number of the variance estimating options. effects are frequently awarded within the kind of theorems; proofs are deleted whilst trivial or whilst a reference is quickly on hand. It applies to massive, advanced surveys; and to supply a simple reference for the survey researcher who's confronted with the matter of estimating variances for genuine survey info.

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Extra info for Introduction to Variance Estimation (Statistics for Social Science and Behavorial Sciences)

Sample text

The Method of Random Groups since the estimators are linear in the Yˆhi . If the Ah are known without error, then the usual random group estimator of variance v(θˆ¯ ∗ ) = 1 k(k − 1) k α=1 (θˆα∗ − θˆ¯ ∗ )2 ˆ The estimator v(θˆ¯ ∗ ) will be biased to the extent that the is unbiased for Var{θ}. measures of Rh are erroneous. So long as the measures of Rh are reasonable, ˆ however, the bias of v(θˆ¯ ∗ ) should be reduced relative to that of v(θ). The results of this subsection were developed given the assumption that n (or n h ) is an integer multiple of k.

Assume n h = km h + qh for h = 1, . . , L, with 0 ≤ qh < k. A straightforward procedure for estimating the variance of θˆ = y¯st is to leave the qh excess observations out of the k random groups. The random group estimators are defined as before, but now θ¯ˆ = k y¯st,α /k = θˆ α=1 ¯ˆ The expectation of the because the excess observations are in θˆ but not in θ. 2, where the n h in the denominator is replaced by n h − qh = km h . 4. 2. 2 with qh excess observations omitted from the k random groups.

2. 1 and may not be strictly satisfied in sampling from finite populations. First, the random variables θˆα must now be independent and identically distributed (θ, σ 2 ) random variables, whereas before they were only held to be uncorrelated with common mean μ. These assumptions, as noted before, do not cause serious problems because the overall sampling scheme (i)–(iii) and the common estimation procedure and measurement process tend to ensure that the more restrictive assumptions are satisfied.

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Introduction to Variance Estimation (Statistics for Social Science and Behavorial Sciences) by Kirk Wolter

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