F Jay Breidt Emeritus

Office: Statistics Building 201

Phone: (970) 491-6786

Google Scholar: https://scholar.google.com/citations?user=i-PzBId6al0C&hl=en&oi=ao

Education

  • 1991: PhD, Statistics, Colorado State University
  • 1989: MS, Statistics, Colorado State University
  • 1987: BA, Mathematics and English Literature, The College of Idaho

About

Jay Breidt, Professor and past Chair of the Department of Statistics at Colorado State University, has expertise in survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models. He received his PhD at Colorado State University in 1991 and spent the first nine years of his career at Iowa State University as an assistant professor and tenured associate professor, before returning to Colorado State in 2000. Breidt has an extensive record of refereed publications. He has presented over 130 invited short courses, conference talks, and academic seminars. Since 1991, his research has been supported continuously by a variety of agencies including the National Science Foundation, National Institutes of Health, Department of Homeland Security, Environmental Protection Agency, US Forest Service, and NASA.  Breidt is the Reviews Editor for the Journal of the American Statistical Association and The American Statistician, and has been an associate editor for seven different journals.  He serves on the US Bureau of the Census Scientific Advisory Committee and has served on six review committees for the National Academy of Sciences.  He is past Chair of the American Statistical Association National Committee on Energy Statistics (an advisory panel for the Energy Information Administration, US Department of Energy) and has served two terms on the Federal Economic Statistics Advisory Committee.  Breidt has received numerous honors, including recognition with a national prize in environmental statistics, elected membership in the International Statistical Institute, and elected fellowship in the American Statistical Association and the Institute of Mathematical Statistics.  Breidt teaches courses at all levels in statistical theory and methods and currently serves as Undergraduate Director for the Department of Statistics.

Publications

  • climate and Soil characteristics Determine Where no-till Management can Store carbon in Soils and Mitigate Greenhouse Gas emissionsStephen M. Ogle, Cody Andrew Alsaker, Jeff Baldock, Martial Bernoux, F Jay Breidt, Brian McConkey, Kristiina Regina, Gabriel G. Vazquez-Amabile Scientific reports, 1, 2019.
  • Estimation of fish consumption rates based on a creel angler survey of an urban river in New Jersey, USABetsy Ruffle, Suzanne Baird, Gemma Kirkwood, F Jay Breidt Human and Ecological Risk Assessment: An International Journal, 2019.
  • Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysisMeng Cao, Wen Zhou, F Jay Breidt, Graham Peers Biometrics, 2019.
  • Minimum Mean Squared Error Estimation of the Radius of Gyration in Small-Angle X-Ray Scattering ExperimentsCody Andrew Alsaker, F Jay Breidt, van der Woerd, Mark J Journal of the American Statistical Association, 525, 2019.
  • Large scale maximum average power multiple inference on time course data of counts and applications to RNA-seq analysisMeng Cao, Wen Zhou, F Jay Breidt, Graham Peers Biometrics.
  • Model-assisted survey estimation with imperfectly matched auxiliary dataInternational Conference of the Thailand Econometrics SocietyF Jay Breidt, Jean D. Opsomer, Chien-Min Huang Springer, 2018.
  • Pilot surveys to improve monitoring of marine recreational fisheries in HawaiʻiHongguang Ma, Tom K. Ogawa, Thomas R. Sminkey, F Jay Breidt, Virginia M. Lesser, Jean D. Opsomer, John R. Foster, David A. Van Voorhees Fisheries research, 2018.
  • Understanding the drivers of sensitive behavior using Poisson regression from quantitative randomized response technique dataMeng Cao, F Jay Breidt, Jennifer N. Solomon, Abu Conteh, Michael Gavin PloS one, 9, 2018.
  • Asymptotics for the maximum sample likelihood estimator under informative selection from a finite populationDaniel Bonnery, F Jay Breidt, Francois Coquet Bernoulli, 2, 2017.
  • Improved Bayesian Inference in the General Projected Normal Distribution of Arbitrary Dimension: Modeling and Bayesian InferenceDaniel Hernandez-Stumpfhauser, F Jay Breidt, van der Woerd, Mark J, others Bayesian Analysis, 1, 2017.
  • Kernel estimation for a superpopulation probability density function under informative selection Bonn\'ery, Daniel, F Jay Breidt, Coquet, Fran\ccois Metron, 3, 2017.
  • Model-Assisted Survey Estimation with Modern Prediction TechniquesF Jay Breidt, Jean D. Opsomer Statistical Science, 2, 2017.
  • Model-assisted survey regression estimation with the lassoKelly S. McConville, F Jay Breidt, Thomas CM Lee, Gretchen G. Moisen Journal of Survey Statistics and Methodology, 2, 2017.
  • Sparse Functional Dynamical Models—A Big Data ApproachEla Sienkiewicz, Dong Song, F Jay Breidt, Haonan Wang Journal of Computational and Graphical Statistics, 2, 2017.
  • The general projected normal distribution of arbitrary dimension: modeling and Bayesian inferenceDaniel Hernandez-Stumpfhauser, F Jay Breidt, van der Woerd, Mark J, others Bayesian Analysis, 1, 2017.
  • Hierarchical Bayesian small area estimation for circular dataDaniel Hernandez-Stumpfhauser, F Jay Breidt, Jean D. Opsomer Canadian Journal of Statistics, 4, 2016.
  • Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroscedastic ErrorsBruce D. Bugbee, F Jay Breidt, van der Woerd, Mark J Journal of Computational and Graphical Statistics, 1, 2016.
  • Nonparametric Regression Methods for Small Area EstimationM Giovanna Ranalli, F Jay Breidt, Jean D. Opsomer Analysis of Poverty Data by Small Area Estimation, 2016.
  • Nonparametric variance estimation under fine stratification: an alternative to collapsed strataF Jay Breidt, Jean D. Opsomer, Ismael Sanchez Borrego Journal of the American Statistical Association, 2016.
  • Pilot Surveys of Shore Fishing on Oahu, HawaiiH. Ma, T. Ogawa, F Jay Breidt, V. Lesser, Jean D. Opsomer, T. Sminkey, C. Hawkins, A. Bagwill, D. Van Voorhees Proceedings of the Survey Research Methods Section, American Statistical Association, 2016.
  • Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete eventsimulationsWalid Saeed Budgaga, Matthew Monte Malensek, Sangmi Lee Pallickara, Neil Harvey, F Jay Breidt, Shrideep Pallickara Future Generation Computer Systems, 2016.
  • Successive Difference Replication Variance Estimation in Two-Phase SamplingJean D. Opsomer, F Jay Breidt, Michael White, Yao Li Journal of Survey Statistics and Methodology, 1, 2016.
  • Variational Approximations for Selecting Hierarchical Models of Circular Data in a Small Area Estimation ApplicationDaniel Hernandez-Stumpfhauser, F Jay Breidt, Jean D. Opsomer STATISTICS IN TRANSITION new series and SURVEY METHODOLOGY, 2016.
  • An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration dataStephen M. Ogle, Kenneth Davis, Thomas Lauvaux, Andrew Schuh, Dan Cooley, Tristram O. West, Linda S. Heath, Natasha L. Miles, Scott Richardson, F Jay Breidt, others Environmental Research Letters, 3, 2015.
  • An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration dataStephen M. Ogle, K Davis, T Lauvaux, Andrew Schuh, Daniel Stuart Cooley, T O. West, L S. Heath, N Miles, S Richardson, F Jay Breidt, J E. Smith, J L. McCarty, K R. Gurney, P Tans, Scott Scott Denning Environmental Research Letters, 2015.
  • Improved Estimation of the Radius of Gyration from Small-Angle X-Ray Scattering DataCody Andrew Alsaker, F Jay Breidt, van der Woerd, Mark J Colorado State University. Libraries, 2015.
  • Statistical approaches for analyzing randomized response technique dataF Jay Breidt, Michael Gavin, Sara G. Lewis Biological Conservation, 187, 2015.
  • Quantifying greenhouse gas sources and sinks in cropland and grazing land systemsStephen M. Ogle, Paul R. Adler, F Jay Breidt, Stephen Del Grosso, Justin Derner, Alan Franzluebbers, Robert Gleason, MA Liebig, Bruce Linquist, GP Robertson, others Quantifying greenhouse gas fluxes in agriculture and forestry: methods for entity-scale inventory. Office of the Chief Economist, US Department of agriculture, Washington DC. Technical Bulletin, 1939, 2014.
  • Using distributed analytics to enable real-time exploration of discrete event simulationsUtility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference onMatthew Monte Malensek, Walid Saeed Budgaga, Sangmi Lee Pallickara, Neil Harvey, F Jay Breidt, Shrideep Pallickara IEEE, 2014.
  • A constrained least-squares approach to combine bottom-up and top-down CO2 flux estimatesDaniel Stuart Cooley, F Jay Breidt, Stephen M. Ogle, Andrew Schuh, Thomas Lauvaux Environmental and ecological statistics, 1, 2013.
  • Advancing national greenhouse gas inventories for agriculture in developing countries: improving activity data, emission factors and software technologyStephen M. Ogle, Leandro Buendia, Klaus Butterbach-Bahl, F Jay Breidt, Melannie Hartman, Kazuyuki Yagi, Rasack Nayamuth, Shannon Lee Spencer, Tom Wirth, Pete Smith Environmental Research Letters, 1, 2013.
  • Nonparametric endogenous post-stratification estimationMark G. Dahlke, F Jay Breidt, Jean D. Opsomer, Ingrid Van Keilegom Statistica Sinica, 2013.
  • Survey design asymptotics for the model-assisted penalised spline regression estimatorKS McConville, F Jay Breidt Journal of Nonparametric Statistics, 3, 2013.
  • Functional model selection for sparse binary time series with multiple inputsCatherine Y. Tu, Dong Song, F Jay Breidt, Theodore W. Berger, Haonan Wang Economic time series: Modeling and seasonality. Boca Raton: Chapman and Hall/CRC, 2012.
  • Penalized balanced samplingF Jay Breidt, G Chauvet Biometrika, 4, 2012.
  • Simulation estimation of quantiles from a distribution with known meanF JayBreidt Journal of Computational and Graphical Statistics, 2012.
  • Uniform convergence of the empirical cumulative distribution function under informative selection from a finite populationDaniel Bonnéry, F Jay Breidt, Francois Coquet, others Bernoulli, 4, 2012.
  • Designing a national soil carbon monitoring network to support climate change policy: a case example for US agricultural landsShannon Lee Spencer, Stephen M. Ogle, F Jay Breidt, J Jeffery Goebel, Keith H. Paustian Greenhouse Gas Measurement and Management, 3-4, 2011.
  • Improved variance estimation for balanced samples drawn via the cube methodF Jay Breidt, Guillaume Chauvet Journal of Statistical Planning and Inference, 1, 2011.
  • Nonparametric Regression Using Kernel and Spline MethodsInternational Encyclopedia of Statistical ScienceJean D. Opsomer, F Jay Breidt Springer Berlin Heidelberg, 2011.
  • A report of the MRIP sampling and estimation project: improved estimation methods for the Access Point Angler Intercept Survey component of the Marine Recreational Fishery Statistics SurveyF Jay Breidt, Han-Lin Lai, Jean D. Opsomer, David A. Van Voorhees NOAA Fisheries Contract Report, Silver Spring, MD, 2010.
  • Estimating uncertainty in N2O emissions from US cropland soilsSJ Del Grosso, Stephen M. Ogle, William J. Parton, F Jay Breidt Global Biogeochemical Cycles, 1, 2010.
  • Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based modelStephen M. Ogle, F Jay Breidt, Mark J. Easter, Steve Williams, Kendrick L. Killian, Keith H. Paustian Global Change Biology, 2, 2010.
  • Spatial LASSO with applications to GIS model selectionHsin-Cheng Huang, Nan-Jung Hsu, David M. Theobald, F Jay Breidt Journal of Computational and Graphical Statistics, 4, 2010.
  • Estimation of the Population Total using the Generalized Difference Estimator and Wilcoxon RanksHugo Andrés Gutiérrez, F Jay Breidt Revista Colombiana de Estadística, 1, 2009.
  • Exact maximum likelihood estimation for non-Gaussian moving averagesNan-Jung Hsu, F Jay Breidt Statistica Sinica, 2009.
  • Nonparametric and semiparametric estimation in complex surveysF Jay Breidt, Jean D. Opsomer Handbook of Statistics, 2009.
  • Predicting Enhanced Vegetation Index (EVI) curves for ecosystem modeling applicationsRam B. Gurung, F Jay Breidt, Amandine Dutin, Stephen M. Ogle Remote Sensing of Environment, 10, 2009.
  • A diagnostic test for autocorrelation in increment-averaged data with application to soil samplingF Jay Breidt, Nan-Jung Hsu, William Coar Environmental and Ecological Statistics, 1, 2008.
  • Endogenous post-stratification in surveys: classifying with a sample-fitted modelF Jay Breidt, Jean D. Opsomer The annals of statistics, 2008.
  • Estimating distribution functions from survey data using nonparametric regressionAlicia A. Johnson, F Jay Breidt, Jean D. Opsomer Journal of Statistical Theory and Practice, 3, 2008.
  • Heteroskedastic Spatial Models with Applications in Computer ExperimentsKe Wang, Wenying Huang, F Jay Breidt, Richard A. Davis2008.
  • Non-parametric small area estimation using penalized spline regressionJean D. Opsomer, Gerda Claeskens, Maria Giovanna Ranalli, Goeran Kauermann, F Jay Breidt Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1, 2008.
  • An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soilsStephen M. Ogle, F Jay Breidt, Mark J. Easter, Steve Williams, Keith H. Paustian Ecological Modelling, 3, 2007.
  • Deriving comprehensive county-level crop yield and area data for US croplandErandathie Lokupitiya, F Jay Breidt, Ravindra Lokupitiya, Steve Williams, Keith H. Paustian Agronomy journal, 3, 2007.
  • Model-assisted estimation of forest resources with generalized additive modelsJean D. Opsomer, F Jay Breidt, Gretchen G. Moisen, Göran Kauermann Journal of the American Statistical Association, 478, 2007.
  • Rank-based estimation for all-pass time series modelsBeth Andrews, Richard A. Davis, F Jay Breidt The Annals of Statistics, 2007.
  • Semiparametric mixed models for increment-averaged data with application to carbon sequestration in agricultural soilsF Jay Breidt, Nan-Jung Hsu, Stephen M. Ogle Journal of the American Statistical Association, 479, 2007.
  • Semiparametric model-assisted estimation for natural resource surveysF Jay Breidt, Jean D. Opsomer, Alicia A. Johnson, M Giovanna Ranalli Survey Methodology, 1, 2007.
  • Struggles with Survey Weighting and Regression Modeling. Comment.F Jay Breidt, Jean D. Opsomer Statistical science, 2, 2007.
  • Bias and variance in model results associated with spatial scaling of measurements for parameterization in regional assessmentsStephen M. Ogle, F Jay Breidt, Keith H. Paustian Global Change Biology, 3, 2006.
  • Controlling the American Community Survey to intercensal population estimatesF JayBreidt Journal of Economic and Social Measurement, 3-4, 2006.
  • Maximum likelihood estimation for all-pass time series modelsBeth Andrews, Richard A. Davis, F Jay Breidt Journal of Multivariate Analysis, 7, 2006.
  • Pile-up probabilities for the Laplace likelihood estimator of a non-invertible first order moving averageTime Series and Related TopicsF Jay Breidt, Richard A. Davis, Nan-Jung Hsu, Murray Rosenblatt, others Institute of Mathematical Statistics, 2006.
  • Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regionsStephen M. Ogle, F Jay Breidt, Keith H. Paustian Biogeochemistry, 1, 2005.
  • Best mean square prediction for moving averagesF Jay Breidt, Nan-Jung Hsu Statistica Sinica, 2005.
  • Diseno de redes de vigilancia de suelos: algunas perspectivas de Estados UnidosProtección del suelo y el desarrollo sostenible: Seminario Europeo: Soria, 15-17 de mayo de 2002F JayBreidt2005.
  • Model-assisted estimation for complex surveys using penalised splinesF Jay Breidt, Gerda Claeskens, Jean D. Opsomer Biometrika, 4, 2005.
  • The potential to mitigate global warming with no-tillage management is only realized whenJ Six, Stephen M. Ogle, F Jay Breidt, RT Conant, AR Mosier, Keith H. Paustian Issue: Global Change Biology, 10 (2), 2004.
  • Bayesian analysis of fractionally integrated ARMA with additive noiseNan-Jung Hsu, F Jay Breidt Journal of Forecasting, 6-7, 2003.
  • Nonparametric regression estimation of finite population totals under two-stage samplingJi-Yeon Kim, F Jay Breidt, Jean D. Opsomer preprint, 2003.
  • Parameter estimation for all-pass time series modelsBeth Andrews, Richard A. Davis, F Jay Breidt2003.
  • A class of nearly long-memory time series modelsF Jay Breidt, Nan-Jung Hsu International Journal of forecasting, 2, 2002.
  • A hierarchical model for estimating distribution profiles of soil textureCase studies in Bayesian statisticsPamela J. Abbitt, F Jay Breidt Springer New York, 2002.
  • Endogenous post-stratification in surveys: classifying with a sample fitted modelF Jay Breidt, Jean D. Opsomer Preprint Series, 02-23, 2002.
  • Television advertising and beef demand: Bayesian inference in a random effects Tobit modelJeremy T. Benson, F Jay Breidt, John R. Schroeter Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 2, 2002.
  • Least absolute deviation estimation for all-pass time series modelsF Jay Breidt, Richard A. Davis, Trindade, A Alexandre Annals of statistics, 2001.
  • Local polynomial regression estimation in two-stage samplingJi-Yeon Kim, F Jay Breidt, Jean D. Opsomer matrix, 2001.
  • Long-range dependent common factor models: A Bayesian approachNan-Jung Hsu, Bonnie K. Ray, F Jay Breidt Communications in Statistics-Theory and Methods, 6, 2001.
  • Modeling Noncausal Autoregressions Using All-Pass FiltersF Jay Breidt, Richard A. Davis2001.
  • Highest density gates for target trackingF Jay Breidt, Alicia L. Carriquiry IEEE Transactions on Aerospace and Electronic Systems, 1, 2000.
  • Local polynomial regression estimators in survey samplingF Jay Breidt, Jean D. Opsomer Annals of Statistics, 2000.
  • The Application of Local Polynomial Regression to Survey Sampling EstimationJean D. Opsomer, F Jay Breidt Citeseer, 2000.
  • Bayesian estimation of common long-range dependent modelsProc. of Seventh Vilnius Conference on Probability Theory and Mathematical StatisticsNan-Jung Hsu, Bonnie K. Ray, F Jay Breidt1999.
  • Design of supplemented panel surveys with application to the National Resources InventoryF Jay Breidt, Wayne A. Fuller Journal of Agricultural, Biological, and Environmental Statistics, 1999.
  • Estimation for supplemented panelsWayne A. Fuller, F Jay Breidt Sankhya: The Indian Journal of Statistics, Series B, 1999.
  • Local Polynomial Regression EstimationF Jay Breidt, Jean D. Opsomer Iowa State University, Department of Economics Staff General Research Papers, 1999.
  • LOCAL POLYNOMIAL REGRESSION ESTIMATION IN A SURVEY OF IOWA SOILSProceedings of the Statistical Computing SectionJean D. Opsomer, F Jay Breidt1999.
  • Modeling common long-range dependence in levels or volatilities using Markov Chain Monte Carlo methodsNan-Jung Hsu, Bonnie K. Ray, F Jay Breidt NJIT Mathematics Department Technical Report, 1998.(available for download at http://m. njit. edu/ borayx/bclrd. ps), 1999.
  • Optimal information acquisition under a geostatistical modelGregory R. Pautsch, Bruce A. Babcock, F Jay Breidt Journal of Agricultural and Resource Economics, 1999.
  • A TIME SERIES APPROACH TO GENETIC TREND ESTIMATIONProceedings of the Section on Bayesian Statistical ScienceSoledad A. Fernandez, Alicia L. Carriquiry, F Jay Breidt1998.
  • Design and estimation for investigating the dynamics of natural resourcesSM Nusser, F Jay Breidt, WA Fuller Ecological Applications, 2, 1998.
  • Extremes of stochastic volatility modelsF Jay Breidt, Richard A. Davis Annals of Applied Probability, 1998.
  • Optimal Sampling Under a Geostatistical ModelGregory R. Pautsch, Bruce A. Babcock, F Jay Breidt1998.
  • Sampling schemes for policy analyses using computer simulation experimentsAlicia L. Carriquiry, F Jay Breidt, PG Lakshminarayan Environmental management, 4, 1998.
  • The detection and estimation of long memory in stochastic volatilityF Jay Breidt, Nuno Crato, Pedro De Lima Journal of econometrics, 1, 1998.
  • Modeling the persistent volatility of asset returnsComputational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997F Jay Breidt, Nuno Crato, Pedro JF de Lima1997.
  • A threshold autoregressive stochastic volatility modelVI Latin American Congress of Probability and Mathematical Statistics (CLAPEM), Valparaiso, ChileF JayBreidt1996.
  • Bayesian Estimation of Genetic TrendAlicia L. Carriquiry, F Jay Breidt1996.
  • Improved quasi-maximum likelihood estimation for stochastic volatility modelsModelling and Prediction Honoring Seymour GeisserF Jay Breidt, Alicia L. Carriquiry Springer New York, 1996.
  • Improved Quasi-Maximum Likelihood for Stochastic Volatility ModelsAlicia L. Carriquiry, F Jay Breidt1996.
  • Sample Design and Estimation Recommendations for Minnesota Fish Contamination StudiesSarah Margaret Nusser, F Jay Breidt Department of Statistics, Iowa State University, 1996.
  • Sampling Approaches for Minnesota Fish Contamination StudiesHeidi S. Shierholz, Sarah Margaret Nusser, F Jay Breidt Department of Statistics, Iowa State University, 1996.
  • Two-phase estimation by imputationF Jay Breidt, Anita McVey, Wayne A. Fuller Journal of the Indian Society of Agricultural Statistics, 1996.
  • Two-Phase Regression Estimation for Policy Analysis Using Computer Simulation ExperimentsAlicia L. Carriquiry, F Jay Breidt, HM Axelson Iowa State University, Department of Economics Staff General Research Papers, 1996.
  • Improved bootstrap prediction intervals for autoregressionsF Jay Breidt, Richard A. Davis, William Dunsmuir Journal of Time Series Analysis, 2, 1995.
  • Markov chain designs for one-per-stratum spatial samplingProceedings of the Section on Survey Research Methods, American Statistical Association, Washington, DCF JayBreidt1995.
  • TWO-PHASE REGRESSION ESTIMATION FOR POLICY ANALYSIS USING COMPUTER SIMULATION EXPERIMENTSProceedings of the Section on Survey Research MethodsF Jay Breidt, Alicia L. Carriquiry1-2, 1995.
  • Approximating the variance of the survey regression estimator using poststratificationProceedings of the Section on Survey Research MethodsKelli A. Leonard, AB An, SM Nusser, F Jay Breidt1994.
  • Modeling long-memory stochastic volatilityPedro JF De Lima, F Jay Breidt, Nuno Crato Johns Hopkins University, Department of Economics, 1994.
  • Regression weighting methods for SIPP dataAmerican Statistical Association 1994 Proceedings of the Section on Survey Research MethodsA An, F Jay Breidt, W Fuller1994.
  • Modeling long-memory stochastic volatilityIowa State University Discussion paperF Jay Breidt, Nuno Crato, PJF de Lima1993.
  • Regression weighting for multiphase samplesF Jay Breidt, Wayne A. Fuller Sankhya: The Indian Journal of Statistics, Series B, 1993.
  • TIME-REVERSIBILITY, IDENTIFIABILITY AND INDEPENDENCE OF INNOVATIONS FOR STATIONARY TIME SERIESF Jay Breidt, Richard A. Davis Journal of Time Series Analysis, 5, 1992.