Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

Genealogies and Gene-ideologies: The Legitimacy of the Beta Israel (Falasha)

The migration of the vast majority of Beta Israel (Falasha) to Israel has been accompanied by major shifts in the discourse regarding their ‘Jewishness’ and ‘Ethiopian-ness’. This article discusses the adoption of traditions of Danite descent in place of traditions of Solomonic descent and the emergence of genetics as the most important identity marker, replacing reckonings of lineage. The first shift is connected to the migration of the Beta Israel from Ethiopia to Israel; the second to changing concepts of what represents ‘proof’ of identity in the modern and post-modern world.     

A general method of constructing E(s2)-optimal supersaturated designs

There has been much recent interest in supersaturated designs and their application in factor screening experiments. Supersaturated designs have mainly been constructed by using the E ( s ²)-optimality criterion originally proposed by Booth and Cox in 1962. However, until now E ( s ²)-optimal designs have only been established with certainty for n experimental runs when the number of factors m is a multiple of n-1 , and in adjacent cases where m = q ( n -1) + r (| r | 2, q an integer). A method of constructing E ( s ²)-optimal designs is presented which allows a reasonably complete solution to be found for various numbers of runs n including n ,=8 12, 16, 20, 24, 32, 40, 48, 64.     

An Algorithm for Unconstrained Quadratically Penalized Convex Optimization

Estimators are often defined as the solutions to data dependent optimization problems. A common form of objective function (function to be optimized) that arises in statistical estimation is the sum of a convex function V and a quadratic complexity penalty. A standard paradigm for creating kernel-based estimators leads to such an optimization problem. This article describes an optimization algorithm designed for unconstrained optimization problems in which the objective function is the sum of a non negative convex function and a known quadratic penalty. The algorithm is described and compared with BFGS on some penalized logistic regression and penalized L ^3/2 regression problems.     

A Unified Way to Represent and Compute the Cumulative Distribution Function for Noncentral t,F, and χ2

This short article shows an unified approach to representing and computing the cumulative distribution function for noncentral t, F, and χ². Unlike the existing algorithms, which involve different expansion and/or recurrence, the new approach consistently represents all the three noncentral cumulative distribution functions as the integral of the normal cumulative distribution function and χ² density function.     

Estimation of Current-Quarter Gross National Product by Pooling Preliminary Labor-Market Data

Two methods of using labor-market data as indicators of contemporaneous gross national product (GNP) are developed. The establishment survey data are used by inverting a partial-adjustment equation for hours. A second GNP forecast can be extracted from the household survey by using Okun's law. Using preliminary rather than final data adds about .2 to .4 percentage point to the expected value of the root mean squared errors and changes the weights that the pooling procedure assigns to the two forecasts. The use of preliminary rather than final data results in a procedure that assigns more importance to the Okun's-law forecast.     

Estimated Data Collection Organization Effect in the National Medical Care Expenditure Survey

The National Medical Care Expenditure Survey, which has a complex survey design, is further complicated by combining two independently drawn national samples of households from the Research Triangle Institute (RTI) and the National Opinion Research Center (NORC). It is assumed that since the structures of these national-area samples are similar, they are thereby compatible and allow for the derivation of statistically equivalent unbiased national estimates of relevant health parameters. In this paper, national parameter estimates of relevant demographic and health measures were separately produced for each of the two survey organizations and a determination was made regarding statistical equivalence across samples. In addition, the data-collection organization effect was estimated and tested within the framework of a multivariate analysis appropriate for complex survey data. The findings consistently revealed a statistically nonsignificant data-collection organization effect when testing for differences in domain estimates of the relevant health parameters under consideration.     

Accent on Teaching Materials

This department includes the two sections New Developments in Statistical Computing and Statistical Computing Software Reviews; suitable contents for each of these sections are described under the respective section heading. Articles submitted for the department, outside the two sections, should not be highly technical and should be relevant to the teaching or practice of statistical computing.

An unbiased estimator of e is used to motivate a simple simulation exercise that requires only observations from the distribution uniform on (0, 1). Antithetic variables are introduced and applied to the simulation problem to give a second unbiased estimator of e with reduced variance.     

Evaluating the Effective Degrees of Freedom of the Delete-a-Group Jackknife

The delete-a-group jackknife is sometimes used when estimating the variances of statistics based on a large sample. We investigate heavily poststratified estimators for a population mean and a simple regression coefficient, where both full-sample and domain estimates are of interest. The delete-a-group (DAG) jackknife employing 30, 60, and 100 replicates is found to be highly unstable, even for large sample sizes. The empirical degrees of freedom of these DAG jackknives are usually much less than their nominal degrees of freedom. This analysis calls into question whether coverage intervals derived from replication-based variance estimators can be trusted for highly calibrated estimates.