作者：
ROSS, DLLauren Ross is an engineer and consultant on environmental and statistical projects. She has a B.S.
M.S. and Ph.D. in civil engineering. Dr. Ross has 15 years of engineering experience particularly in the design of ground water monitoring systems data analysis computer modeling and computer program development. She has also worked on non-point source pollution assessment for storm water runoff. In addition to industry and governmental clients Dr. Ross provides engineering consulting to environmental groups including Save Barton Creek Association and the SOS Coalition in Austin Texas.
A closed-loop degaussing technique for MCM (mine countermeasure) class vessels has been developed and tested on a minesweeper engine. The system was designed to accurately predict and degauss the off-board magnetic fi...
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A closed-loop degaussing technique for MCM (mine countermeasure) class vessels has been developed and tested on a minesweeper engine. The system was designed to accurately predict and degauss the off-board magnetic field signatures from measurements taken on-board a non-magnetic hull naval signature prediction algorithm have been investigated. An on-board sensor configuration, that requires only four single axis gradiometers, has been shown to accurately predict the magnetic signatures of the full-scale engine.
作者：
MCNICHOLS, RJDAVIS, CBRoger J. McNichols is a professor of industrial engineering at the University of Toledo (Department of Industrial Engineering
University of Toledo Toledo OH 43606). After receiving his Ph.D in industrial engineering from The Ohio State University he joined the faculty of Texas A and M University where he directed the Maintainability Engineering Graduate Program at Red River Army Depot. At UT he has served as associate dean of engineering and as chairman of the Systems engineering doctoral program. His research and consulting interests include reliability quality control manufacturing mathematical modeling and applied statistics. Charles B. Davis is an associate professor of mathematics at the University of Toledo (Department of Mathematics
University of Toledo Toledo OH 43606). After receiving his M.S. in mathematics and statistics and his Ph.D. in statistics from the University of New Mexico he joined the Mathematics Department at UT where he established the graduate program in statistics. His research and consulting interests include statistical modeling statistical computation simultaneous inference and data analysis.
Ground water monitoring presents interesting statistical challenges, including controlling the risk of entering compliance monitoring, incorporating all modes of inherent variability into the statistical model on whic...
Ground water monitoring presents interesting statistical challenges, including controlling the risk of entering compliance monitoring, incorporating all modes of inherent variability into the statistical model on which tests are based, and taming the detection limit problem, all while maintaining demonstrable sensitivity to real contamination. Some of these challenges exceed textbook statistics considerably, even when considered alone, and good solutions are scarce. When these challenges are combined, the task of developing good statistical procedures or good regulations can be formidable. This article presents a number of realities of ground water monitoring that should be considered when developing statistical procedures. Recommendations made for addressing these realities include the following: (1) the false positive rate should be controlled on a facility-wide basis, rather than per well or per parameter as required in the proposed regulation (40 CFR §264); (2) multiple comparisons with control procedures are preferable to analysis of variance (ANOVA) for controlling the overall false positive rate; (3) retests can be made an explicit part of the statistical procedure in order to increase power and decrease sensitivity to distribution shape assumptions; (4) commonly used simple methods of handling below detection limit data with parametric tests, including Cohen's procedure as implemented in the U.S. EPA's Technical Enforcement Guidance Document (TEGD), should probably be avoided; (5) the statistical properties of practical quantitation limits for non-naturally occurring compounds should be studied carefully; and (6) so long as the facility-wide false positive rate is controlled, better sensitivity to real contamination is obtained by monitoring fewer well-chosen parameters at a smaller number of well-chosen locations. An evaluation of the proposed revised §264 regulation with respect to these realities reveals that it seems to be a definite improvement over the
作者：
RICE, GBRINKMAN, JMULLER, DGeorge F. Rice
geohydrologist joined Sergent Hauskins & Beckwith Geotechnical Engineers Inc. (4700 Lincoln Rd. N.E. Albuquerque NM 87109) in 1983 and is working on the UMTRA Project. His duties include characterization of low-level nuclear waste sites design of monitor well and vadose zone monitoring networks application of ground water transport codes and prediction of the effects of remedial actions on ground water. Dianna L. Muller
civil engineer joined the UMTRA Project in 1985 as a member of the Hydrological Services Group. She works for Roy F. Weston Inc. (5301 Central Ave. N.E. Suite 1000 Albuquerque NM 87108). Her duties include primary responsibility for laboratory water quality data analyses meeting UMTRA quality assurance specifications and support to the staff geohydrologists. James E. Brinkman
senior geohydrogeologist joined R.oy F. Weston Inc. (5301 Central Ave. N.E. Suite 1000 Albuquerque NM 87108) in February 1987 as senior geohydrologist. His duties include project management for hydrogeologic field investigations at mill tailings and hazardous and mixed waste sites design of ground water monitoring networks design of contaminant control and removal measures and predictive analysis of future impacts utilizing computer modeling techniques.
Ground water quality investigations require reliable chemical analyses of water samples. Unfortunately, laboratory analytical results are often unreliable. The Uranium Mill Tailings Remedial Action (UMTRA) Project'...
Ground water quality investigations require reliable chemical analyses of water samples. Unfortunately, laboratory analytical results are often unreliable. The Uranium Mill Tailings Remedial Action (UMTRA) Project's solution to this problem was to establish a two-phase quality assurance program for the analysis of water samples. In the first phase, eight laboratories analyzed three solutions of known composition. The analytical accuracy of each laboratory was ranked and three laboratories were awarded contracts. The second phase consists of on-going monitoring of the reliability of the selected laboratories. The following conclusions are based on two years of experience with the UMTRA Project's Quality Assurance Program: • The reliability of laboratory analyses should not be taken for granted. • Analytical reliability may be independent of the prices charged by laboratories. • Quality assurance programs benefit both the customer and the laboratory.
Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. Since the success of the fast Fourier transform algorithm, the analysis of serial ...
Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. Since the success of the fast Fourier transform algorithm, the analysis of serial auto- and cross-correlation in the frequency domain has helped us to understand the dynamics in many serially correlated data without necessarily needing to develop complex parametric models. In this work, we give a nonexhaustive review of the mostly recent nonparametric methods of spectral analysis of multivariate time series, with an emphasis on model-based approaches. We try to give insights into a variety of complimentary approaches for standard and less standard situations (such as nonstationary, replicated, or high-dimensional time series), discuss estimation aspects (such as smoothing over frequency), and include some examples stemming from life science applications (such as brain data).
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