National Center for Statistics and Analysis collected technical studies. Volume I: accident data analysis of vehicle crashworthiness - ten papers.

Mode of access: Internet.

Establishment of a National Accident Sampling System (NASS) team in Ashley, Chicot, Desha, Drew, and Lincoln Counties, Arkansas. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The National Accident Sampling System - a status report. Volume I: objectives of the National Accident Sampling System.

Mode of access: Internet.

The National Accident Sampling System - a status report. Volume II: plan for a pilot study.

Mode of access: Internet.

The National Accident Sampling System - a status report. Volume III: implementation of NASS subsystems.

Mode of access: Internet.

National Center for Statistics and Analysis collected technical studies. Volume II: accident data analysis of occupant injuries and crash characteristics - eight papers.

Mode of access: Internet.

Establishment and operation of the National Accident Sampling System (NASS) team within the cities of Ft. Lauderdale/Hollywood, Florida. Final report.

National Highway Traffic Safety Administration, National Center for Statistics and Analysis, Washington, D.C.

The National Accident Sampling System.

Mode of access: Internet.

The National Accident Sampling System - a status report. Volume IV: implementation schedule and resource requirements.

Mode of access: Internet.

National Accident Sampling System - pilot study. Final report.

Mode of access: Internet.

Sample registration of vital events with verbal autopsy: a renewed commitment to measuring and monitoring vital statistics

Registration of births, recording deaths by age, sex and cause, and calculating mortality levels and differentials are fundamental to evidence-based health policy, monitoring and evaluation. Yet few of the countries with the greatest need for these data have functioning systems to produce them despite legislation providing for the establishment and maintenance of vital registration. Sample vital registration (SVR), when applied in conjunction with validated verbal autopsy, procedures and implemented in a nationally representative sample of population clusters represents an affordable, cost-effective, and sustainable short- and medium-term solution to this problem. SVR complements other information sources by producing age-, sex-, and cause-specific mortality data that are more complete and continuous than those currently available. The tools and methods employed in an SVR system, however, are imperfect and require rigorous validation and continuous quality assurance; sampling strategies for SVR are also still evolving. Nonetheless, interest in establishing SVR is rapidly growing in Africa and Asia. Better systems for reporting and recording data on vital events will be sustainable only if developed hand-in-hand with existing health information strategies at the national and district levels; governance structures; and agendas for social research and development monitoring. If the global community wishes to have mortality measurements 5 or 10 years hence, the foundation stones of SVR must be laid today.

Heavy tails, importance sampling and cross-entropy

We consider the problem of estimating P(Yi + (...) + Y-n > x) by importance sampling when the Yi are i.i.d. and heavy-tailed. The idea is to exploit the cross-entropy method as a toot for choosing good parameters in the importance sampling distribution; in doing so, we use the asymptotic description that given P(Y-1 + (...) + Y-n > x), n - 1 of the Yi have distribution F and one the conditional distribution of Y given Y > x. We show in some specific parametric examples (Pareto and Weibull) how this leads to precise answers which, as demonstrated numerically, are close to being variance minimal within the parametric class under consideration. Related problems for M/G/l and GI/G/l queues are also discussed.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Finite Sampling Exponential Bounds

Thesis (Ph.D.)--University of Washington, 2016-08

Yellow Perch population assessment in Southwestern Lake Michigan, including evaluation of sampling techniques and identification of the factors that determine Yellow Perch year-class strength April 1, 2004 - March 31, 2005

ID: 8528; Annual Report to Division of Fisheries, Illinois Department of Natural Resources; F-123-R-11