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Search Publications by: Andrew L Rukhin (Fed)

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Displaying 1 - 25 of 56

Quantifying Uncertainty in Accelerometer Sensitivity Studies

June 22, 2015
Andrew L. Rukhin, David J. Evans
Key Comparisons of accelerometers sensitivity measurement are performed to compare the sensitivity of linear accelerometers. The key comparison reference value (KCRV) for charge sensitivity as a function of frequency and the accompanying uncertainty are

Decision-Theoretic Issues in Heterogeneity Variance Estimation

February 8, 2015
Andrew L. Rukhin
In a random-effects setting of meta-analysis the between-study variance estimation is studied from the decision-theoretic point of view. The problem concerns simultaneous inference on curve-confined natural parameters of independent heterogeneous chi

Decision-Theoretic Aspects of Meta-Analysis

April 1, 2014
Andrew L. Rukhin
In the random-effects model of meta-analysis a canonical representation of the restricted likelihood function is obtained. The relationship between the common mean estimators and the heterogeneity variance estimators is explored via this representation. An

Bounds on Elementary Symmetric Functions

January 31, 2014
Andrew L. Rukhin
Tight bounds on an elementary symmetric function are established for given values of these polynomials of lower order.

Bayes Estimators of Heterogeneity Variance and T-Systems

January 8, 2014
Andrew L. Rukhin
The considered problem concerns simultaneous inference for curve-confined natural parameters of independent, heterogeneous gamma-random variables with known shape parameters. The meta-analysis motivated loss function is suggested and some properties of the

Estimating Heterogeneity Variance in Meta-Analysis

August 6, 2012
Andrew L. Rukhin
Several new estimators of the between-study variability in a heterogeneous random effects meta-analysis model are derived. One of them can be interpreted as the empirical Bayes procedure for a diffuse prior with the given prior mean. Another is the

Evaluation of Self Diffusion Data Using Weighted Means Statistics

June 1, 2011
Carelyn E. Campbell, Andrew L. Rukhin
Several CALPHAD-based assessments of the self-diffusion mobility of Ni in pure Ni and Al in pure Al have been published. In an effort to determine which assessment best represents the experimental data, weighted means statistics are used to determine

Estimating Common Parameters in Heterogeneous Random Effects Models

April 13, 2011
Andrew L. Rukhin
A question of fundamental importance for meta-analysis of heterogeneous data studies is how to form a best consensus estimator of common parameters, and what uncertainty to attach to the estimate. This issue is addressed for a class of unbalanced linear

Limit of detection determination for censored samples.

January 11, 2011
Andrew L. Rukhin, Daniel V. Samarov
The problem of setting the limit of detection is considered for censored samples and heterogeneous errors. After formal definitions of the critical level and of the method detection limit, we introduce simplified maximum likelihood-type estimators. The

Reducing Data Nonconformity in Linear Models

January 3, 2011
Andrew L. Rukhin
Stein phenomenon Summary Several procedures designed to reduce nonconformity in interlaboratory studies by shrinking data toward a consensus matrix weighted mean are suggested. Some of them are shown to have a smaller quadratic risk than the vector sample

Laplace Random Effects Models for Interlaboratory Studies

November 25, 2010
Andrew L. Rukhin, Antonio M. Possolo
A model is introduced for measurements obtained in collaborative interlaboratory studies, comprising measurement errors and random laboratory effects that have Laplace distributions, possibly with heterogeneous, laboratory-specific variances. Estimators

A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications

September 16, 2010
Lawrence E. Bassham, Andrew L. Rukhin, Juan Soto, James R. Nechvatal, Miles E. Smid, Stefan D. Leigh, M Levenson, M Vangel, Nathanael A. Heckert, D L. Banks
This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may be used in many cryptographic applications, such as the generation of key material. Generators suitable for use in

Confidence Regions for Parameters of Linear Models

April 1, 2010
Andrew L. Rukhin
A method is suggested for constructing a conservative confidence region for the parameters of a general linear model. In meta-analytical applications, when the results of independent but heterogeneous studies are to be combined, this region can be employed