Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data

To gain insight into how characteristics of an establishment are associated with nonresponse, a recursive partitioning algorithm is applied to the Occupational Employment Statistics May 2006 survey data to build a regression tree. The tree models an establishment's propensity to respond to the survey given certain establishment characteristics. It provides mutually exclusive cells based on the characteristics with homogeneous response propensities. This makes it easy to identify interpretable associations between the characteristic variables and an establishment's propensity to respond, something not easily done using a logistic regression propensity model. We test the model obtained using the May data against data from the November 2006 Occupational Employment Statistics survey. Testing the model on a disjoint set of establishment data with a very large sample size $(n=179,360)$ offers evidence that the regression tree model accurately describes the association between the establishment characteristics and the response propensity for the OES survey. The accuracy of this modeling approach is compared to that of logistic regression through simulation. This representation is then used along with frame-level administrative wage data linked to sample data to investigate the possibility of nonresponse bias. We show that without proper adjustments the nonresponse does pose a risk of bias and is possibly nonignorable.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS521 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian Estimation Under Informative Sampling

Bayesian analysis is increasingly popular for use in social science and other application areas where the data are observations from an informative sample. An informative sampling design leads to inclusion probabilities that are correlated with the response variable of interest. Model inference performed on the observed sample taken from the population will be biased for the population generative model under informative sampling since the balance of information in the sample data is different from that for the population. Typical approaches to account for an informative sampling design under Bayesian estimation are often difficult to implement because they require re-parameterization of the hypothesized generating model, or focus on design, rather than model-based, inference. We propose to construct a pseudo-posterior distribution that utilizes sampling weights based on the marginal inclusion probabilities to exponentiate the likelihood contribution of each sampled unit, which weights the information in the sample back to the population. Our approach provides a nearly automated estimation procedure applicable to any model specified by the data analyst for the population and retains the population model parameterization and posterior sampling geometry. We construct conditions on known marginal and pairwise inclusion probabilities that define a class of sampling designs where $L_{1}$ consistency of the pseudo posterior is guaranteed. We demonstrate our method on an application concerning the Bureau of Labor Statistics Job Openings and Labor Turnover Survey.Comment: 24 pages, 3 figure

The design and development of a release mechanism for space shuttle life-science experiments

The design, development, and testing of a release mechanism for use in two life science experiments on the Spacelab 1, 4, and D1 missions is described. The mechanism is a self latching ball lock device actuated by a linear solenoid. An unusual feature is the tapering of the ball lock plunger to give it a near constant breakout force for release under a wide range of loads. The selection of the design, based on the design requirements, is discussed. A number of problems occurred during development and test, including problems caused by human factors that became apparent after initial delivery for crewtraining sessions. These problems and their solutions are described to assist in the design and testing of similar mechanisms

Efficient non-linear 3D electrical tomography reconstruction

Non-linear electrical tomography imaging can be performed efficiently if certain optimisations are applied to the computational reconstruction process. We present a 3D non-linear reconstruction algorithm based on a regularized conjugate gradient solver and discuss the optimisations which we incorporated to allow for an efficient and accurate reconstruction. In particular, the application of image smoothness constraints or other regularization techniques and auto-adaptive mesh refinement are highly relevant. We demonstrate the results of applying this algorithm to the reconstruction of a simulated material distribution in a cubic volume

Optimal finite element modelling and efficient reconstruction in non-linear 3D electrical resistance tomography

Electrical Impedance Tomography can provide images with well-defined characteristics using a fully non-linear reconstruction process when appropriate constraints are imposed on the solution to allow the ill-posed inverse problem to be solved. Using appropriate finite element discretizations for forward solution and inverse problem offers additional advantages in the image reconstruction process, such as (a) inclusion of prior knowledge, (b) generic model templating to adapt to, for example, individual head shapes, and (c) obtaining accurate results without unnecessary computational overhead. We have developed an efficient 3D non-linear reconstruction algorithm based on a regularized inverse conjugate gradient solver which incorporates (a) local image smoothness constraints, and (b) a number of optimisations which reduce the computing power required to obtain an accurate solution. We show results from applying this to various problems which arise in medical resistivity reconstruction given only surface potential measurements and demonstrate the importance of the FE discretization. Keywords: 3D non-linear electrical impedance tomography, FE template modelling, optimal finite element meshes, 3D visualization, FE discretization

A simple model of electron beam initiated dielectric breakdown

A steady state model that describes the internal charge distribution of a planar dielectric sample exposed to a uniform electron beam was developed. The model includes the effects of charge deposition and ionization of the beam, separate trap-modulated mobilities for electrons and holes, electron-hole recombination, and pair production by drifting thermal electrons. If the incident beam current is greater than a certain critical value (which depends on sample thickness as well as other sample properties), the steady state solution is non-physical