 Analysis of Categorical Data Under a Full ModelParimal Mukhopadhyay ()
Chapter Chapter 4 in Complex Surveys, 2016, pp 97-133 from Springer Abstract: Abstract Nowadays, large-scale sample surveys are often conducted to collect data to test different hypotheses in natural and social sciences. Such surveys often use stratified multistage cluster design. Data obtained through such complex survey designs are not generally independently distributed and as a result multinomial models do not hold in such cases. Thus, the classical Pearson statistic and the related usually used test statistic would not be valid tools for testing different hypotheses in these circumstances. Here we propose to investigate the effect of stratification and clustering on the asymptotic distribution of Pearson statistic, log-likelihood ratio statistic for testing goodness-of-fit (simple hypothesis), independence in two-way contingency tables, and homogeneity of several populations. Keywords: Pearson’s statistic ( $$X_P^2)$$ X P 2 ); Log-likelihood ratio statistic; Design-based Wald statistic; Goodness-of-fit tests; Tests of homogeneity; Tests of independence; Rao–Scott corrections to $$X_P^2$$ X P 2; Fay’s jackknifed statistic (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0871-9_4 Ordering information: This item can be ordered from DOI: 10.1007/978-981-10-0871-9_4 Access Statistics for this chapter More chapters in Springer Books from Springer |
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