By Anna Maria Paganoni, Piercesare Secchi
The ebook 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, machine in depth equipment and purposes: community inference from the research of excessive dimensional information; new advancements for bootstrapping complicated information; regression research for measuring the downsize reputational hazard; statistical tools for study at the human genome dynamics; inference in non-euclidean settings and for form facts; Bayesian tools for reliability and the research of advanced info; methodological concerns in utilizing administrative facts for scientific and epidemiological examine; regression types with differential regularization; geostatistical equipment for mobility research via cell phone information exploration. This quantity is the results of a cautious choice one of the contributions awarded on the convention "S.Co.2013: advanced facts modeling and computationally extensive equipment for estimation and prediction" held on the Politecnico di Milano, 2013. the entire papers released right here were carefully peer-reviewed.
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Extra info for Advances in Complex Data Modeling and Computational Methods in Statistics
Arena et al. Generally speaking, reputation can be conceived as the system of stakeholders’ perceptions and expectations towards the corporation , and reputational risk can be defined as the risk of having this system of perceptions/expectations altered or damaged. From this perspective, the ability of a company of handling a crisis depends not only on the quality and timeliness of its decision making, but also on how its actions are perceived by its stakeholders [9, 10]. However, perceptions and expectations are hardly measurable, leading to the need of defining an ad hoc approach to measure reputational risk.
Finally, for what concerns point 3, departures from the ideal integer-weights case are obtained inducing an increasing level of perturbation on the inclusion probabilities k ; k 2 U . For SRS, this is achieved by means of small systematic modifications to the population size N , leading to the slight departure from the stated sample fractions described above, together with suitable element-wise deletions from U in order to retain the chosen population mean and (relative) variability dictated by each scenario.
3, whose results were presented in Tables 3, 4, 5, and 6. Once again, we separate the two sampling designs with equal and unequal inclusion probabilities (respectively, SRS and CPS, defined in Sect. 3), in order to better highlight the most significant differences we found in their respect regarding the Monte Carlo quantities defined in Eqs. (1)–(3). SRS—Tables 3 and 4 • %RBN – integer-weights case: as expected, no bias was detected on the estimation of N by N ; – systematic rounding: (small) bias was found, that worsen proportionally with sample size.