This research involves the estimation of the total number of classes of objects in a region by sampling sectors or quadrats. For each selected quadrat, the classes are recorded. From these data, estimates and/or confidence limits for the number of classes in the region are developed. Models which differ in their methods of sampling (simple random sampling or stratified random sampling) and in their assumptions concerning the classes are investigated. / We present three simple random sampling models: a mixture model, a Bayesian lower limit model, and a j$\sp{\rm th}$-order bootstrap bias-correction model. For the mixture model, we develop an asymptotic confidence relation for the number of classes as well as discuss optimal sampling designs. For the next model, we obtain an asymptotic Bayesian lower limit for the expected number of unobserved classes with the limit being robust to the prior on $\theta$, the number of classes. Our j$\sp{\rm th}$-order bootstrap bias-corrected estimator of $\theta$ extends the (first-order) bootstrap estimator reported by Smith and van Belle (1984). / Then we contrast stratified random sampling with simple random sampling and demonstrate that the expected number of observed classes can be greatly increased by stratification. We also extend some components of the simple random sampling models to stratified random sampling. / Source: Dissertation Abstracts International, Volume: 51-07, Section: B, page: 3444. / Major Professor: Duane Anthony Meeter. / Thesis (Ph.D.)--The Florida State University, 1990.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_78280 |
| Contributors | Norris, James Lawrence, III., Florida State University |
| Source Sets | Florida State University |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | 174 p. |
| Rights | On campus use only. |
| Relation | Dissertation Abstracts International |
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