scholarly journals Estimating the input of a Lévy-driven queue by Poisson sampling of the workload process

Bernoulli ◽  
2019 ◽  
Vol 25 (4B) ◽  
pp. 3734-3761 ◽  
Author(s):  
Liron Ravner ◽  
Onno Boxma ◽  
Michel Mandjes
Keyword(s):  
Poisson Sampling

scholarly journals A metodologia de amostragem do Saeb

2019 ◽  
Vol 81 (197) ◽  
Author(s):  
Marcus M. Riether ◽  
Raíssa Rauter
Keyword(s):  
International Research ◽  
Target Population ◽  
Random Numbers ◽  
Sampling Methodology ◽  
Complex Samples ◽  
Poisson Sampling ◽  
Sampling Schemes ◽  
Data Analyses ◽  
Research Institutes

Apresenta, sob um enfoque simplificado, a metodologia de amostragem do Saeb, enfatizando particularidades relacionadas com o ciclo de 2001 e buscando esclarecer o leitor em relação a alguns dos pontos considerados mais importantes, tais como população de referência, estágios, esquemas de seleção de unidades amostrais e métodos de análise de dados de amostras complexas. Tópicos como precisão amostral, que envolvem discussões corriqueiras sobre a não-publicação de resultados individuais por escola ou a repetição de escolas que participaram do Saeb 1999, também são discutidos. Citam-se, ainda, casos de institutos de pesquisa sediados em outros países e que utilizam metodologias de amostragem semelhantes a do Saeb. Por fim, são apresentadas sugestões para o Saeb em suas futuras realizações. Palavras-chave: amostras complexas; coordenação de amostras; amostragem seqüencial de Poisson; números aleatórios permanentes. Abstract This paper presents a simplified version of the Saeb sampling methodology, emphasizing particularities of its cycle of 2001, and aiming at making clear to the reader some of its main points, such as target population, stages, sampling schemes and data analyses methods applied to complex samples. Topics like precision of estimates, that involve customary discussions about the avoidance in publishing schools' individual results or the sample overlap with the Saeb 1999 are also discussed. Cases of international research institutes that make use of sampling methodologies similar to that of Saeb are also mentioned. To finish, we present suggestions to Saeb in its future realizations. Keywords: complex samples; sample overlap; sequential Poisson sampling; permanent random numbers.

scholarly journals Comparison of estimators in one-phase two-stage Poisson sampling in forest inventories

2012 ◽  
Vol 42 (10) ◽  
pp. 1865-1871 ◽  
Author(s):  
Daniel Mandallaz ◽  
Alexander Massey
Keyword(s):  
Small Area ◽  
Two Stage ◽  
Random Sample Size ◽  
Poisson Sampling ◽  
Second Stage ◽  
Forest Inventories ◽  
Unbiased Estimates ◽  
The Mean ◽  
Concentric Circles ◽  
Better Than

In the context of Poisson sampling, numerous adjustments to classical estimators have been proposed that are intended to compensate for inflated variance due to random sample size. However, such adjustments have never been applied to extensive forest inventories. This work investigates the performances of four estimators for the timber volume in one-phase two-stage forest inventories, where trees in the first stage are selected, at the plot level, by concentric circles or angle-count methods and a subset thereof are selected by Poisson sampling for further measurements to get a better estimation. The original two-stage estimator is the sum of two components: the first is the mean of Horwitz–Thompson estimators using simple volume approximations, based on diameter and species alone, of all first-stage trees in each inventory plot, and the second is the mean of Horwitz–Thompson estimators based on the differences between the simple volume approximations and refined volume determinations based on further diameter and height measurements on the second-stage trees within each inventory plot. This two-stage estimator is particularly useful because it provides unbiased estimates even if the simple prediction model is not correct, which is particularly important for small area estimation. The other three estimators rely on adjustments of the second component of the original estimator that are adapted from estimators proposed in the literature by L.R. Grosenbaugh and C.-E. Särndal. It turns out that these adjustments introduce a negligible bias and that the original simple estimator performs just as well or even better than the new estimators with respect to the variance.

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