U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Andrew L Rukhin (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 76 - 100 of 112

Conservative Confidence Intervals Based on Weighted Means Statistics

April 15, 2007
Author(s)
Andrew L. Rukhin
For weighted means estimators of the common mean of several normal populations associated (conservative) confidence intervals are constructed. These intervals are compared to several traditional confidence bounds. Monte Carlo simulation results of these

Estimating Common Vector Parameters in Interlaboratory Studies

March 13, 2007
Author(s)
Andrew L. Rukhin
The primary goal of this work is to extend two methodsof random effects models to multiparameter situation. These methods comprise the DerSimonian-Laird estimator, arising in the meta-analysis, and the Mandel-Paule algorithm widely used in interlaboratory

Nonparametric Inference for Balanced Randomization Designs

March 1, 2007
Author(s)
Andrew L. Rukhin
The properties of two balanced randomization schemes which allocate the known number of subjects among several treatments are compared. According to the first procedure, the so-called truncated multinomial randomization design, the allocation process

Association Characteristics in Spatial Statistics

September 12, 2006
Author(s)
Andrew L. Rukhin
A coefficient of association for two spatial sequences is suggested. Some properties of this characteristic are discussed. By using the Central Limit Theorem for stationary random fields its limiting asymptotic normality is derived for any error

Stationary Distributions in the Atom-on-Demand Problem

September 1, 2006
Author(s)
I Bebu, Andrew L. Rukhin
In this note a probability model for the number of atoms in a magneto-optical trap is suggested. We study an ergodic Markov Chain for the number of atoms in the trap under a feedback regime for different load distributions. Formulas for the stationary

Statistical Aspects of Linkage Analysis in Interlaboratory Studies

November 9, 2005
Author(s)
W Strawderman, Andrew L. Rukhin
This paper investigates issues that arise in statistical inference in interlaboratory studies known as Key Comparisons when one has to link several comparisons to or through existing studies. A new approach to the analysis of such a data is proposed using

Fusion of Biometric Algorithms in Recognition Problem

January 1, 2005
Author(s)
Andrew L. Rukhin, Igor Malioutov
This note concerns the mathematical aspects for several biometric algorithms in the recognition or identification problem. It is assumed that a biometric signature is presented to a system which compares it with a database of signatures of known

Performance of Service-Discovery Architectures in Response to Node Failures

June 1, 2003
Author(s)
Christopher E. Dabrowski, Kevin L. Mills, Andrew L. Rukhin
Current trends suggest future software systems will rely on service-discovery protocols to combine and recombine distributed services dynamically in reaction to changing conditions. We investigate the ability of selected designs for service-discovery

Calibration Experiments of a Laser Scanner

September 1, 2002
Author(s)
Geraldine S. Cheok, Stefan D. Leigh, Andrew L. Rukhin
The potential applications of laser scanners or LADARs (Laser Detection and Ranging) are numerous, and they cross several sectors of the industry construction, large-scale manufacturing, remote sensing, national defense. A LADAR is an instrument which can

Transformation, Ranking, and Clustering for Face Recognition Algorithm Comparison

March 1, 2002
Author(s)
Stefan D. Leigh, Nathanael A. Heckert, Andrew L. Rukhin, J G. Phillips, Elaine M. Newton, M Moody, K Kniskern, S Heath
The performance of face recognition algorithms is recently of increased interest, although to date empirical analyses of algorithms have been limited to rank-based scores such a cumulative match score and receiver operating characteristic. This paper

Dependence Characteristics of Face Recognition Algorithms

January 1, 2002
Author(s)
Andrew L. Rukhin, Patrick J. Grother, P J. Phillips, Stefan D. Leigh, E M. Newton, Nathanael A. Heckert
Nonparametric statistics for quantifying dependence between the output rankings of face recognition algorithms are described. Analysis of the archived results of a large face recognition study shows that even the better algorithms exhibit significantly