Download e-book for kindle: Advances in Complex Data Modeling and Computational Methods by Anna Maria Paganoni, Piercesare Secchi

By Anna Maria Paganoni, Piercesare Secchi

ISBN-10: 3319111485

ISBN-13: 9783319111483

ISBN-10: 3319111493

ISBN-13: 9783319111490

The publication is addressed to statisticians operating on the leading edge of the statistical research of advanced and excessive dimensional information and gives a large choice of statistical versions, computing device in depth tools and functions: community inference from the research of excessive dimensional information; new advancements for bootstrapping complicated facts; regression research for measuring the downsize reputational possibility; statistical equipment for study at the human genome dynamics; inference in non-euclidean settings and for form info; Bayesian equipment for reliability and the research of advanced facts; methodological concerns in utilizing administrative facts for medical and epidemiological learn; regression types with differential regularization; geostatistical tools for mobility research via cell phone information exploration. This quantity is the results of a cautious choice one of the contributions provided on the convention "S.Co.2013: complicated facts modeling and computationally in depth equipment for estimation and prediction" held on the Politecnico di Milano, 2013. the entire papers released the following were conscientiously peer-reviewed.

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Extra resources for Advances in Complex Data Modeling and Computational Methods in Statistics

Sample text

1; specifically, we show only the upward deviations from N D 600, since no difference in absolute value has been observed with respect to the quantities of interest when considering the downward ones. 28 F. Andreis and F. 2 Unequal-Probability Sampling (CPS) Tables 5 and 6 contain the results of the simulation study for, respectively, systematic rounding and randomization-based (binomial) rounding under the CPS design. The column headers report the extent of the perturbation induced on the inclusion probabilities, from 25 % to 100 %, as described in Sect.

Andreis and F. Mecatti negligible bias regardless of population variability and extent of rounding. It seems, again, to be growing with sample size when variability is low. 5 – As observed in the SRS case, %RBV grows steadily with population variability and has negative sign, indicating a persistent under-estimation of the true variance of yNHT ; – systematic rounding: the ratio of %RBV for systematic rounding over %RBV for the reference situation is consistently higher than one, suggesting a significant increase in relative bias of variance estimation when rounding is needed: the effect is particularly evident with respect to the larger sample size considered (n D 30), where all the ratios are higher, leading to double bias in one scenario.

Int. Stat. Rev. 80(1), 127–148 (2012) 4. : Bootstrap methods for finite populations. J. Am. Stat. Assoc. 89, 1282–1289 (1994) 5. : A Bootstrap method for finite population. Sankhya 47(A), 399–405 (1985) 6. : Méthodes de Bootstrap en population finie. D. Dissertation, Laboratoire de statistique d’enquêtes, CREST-ENSAI, Université de Rennes 2. pdf (2007) 7. : Biased Urn Theory. R package vignette. pdf (2013) 8. : Median estimation in sample Surveys. In: Proceedings of SSRM. American Statistical Society, pp.

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Advances in Complex Data Modeling and Computational Methods in Statistics by Anna Maria Paganoni, Piercesare Secchi


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