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Research

Publications

Book

Stein, M.L. (1999) Statistical Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York.

Papers

[1] Stein, M. L. (1984). System parameters governed by jump processes: a model for removal of air polluants. Advances in Applied Probability, 16, 603--617.

[2] Stein, M. L. (1986). An efficient method of sampling for statistical circuit design. IEEE Transactions on Computer-Aided Design, CAD-5, 23--29.

[3] Stein, M. L. (1986). A modification of minimum norm quadratic estimation of a generalized covariance function for use with large data sets. Mathematical Geology, 18, 625--633.

[4] Stein, M. L. (1986). A simple model for spatial-temporal processes. Water Resources Research, 22, 2107--2110.

[5] Stein, M. L. (1987). Large sample properties of simulations using Latin hypercube sampling. Technometrics, 29, 143--151. Correction, 32, 367.

[6] Stein, M. L. (1987). Gaussian approximations to conditional distributions for multigaussian processes. Mathematical Geology, 19, 387--405.

[7] Stein, M. L. (1987). Minimum norm quadratic estimation of spatial variograms. Journal of the American Statistical Association, 82, 765--772.

[8] Stein, M. L. (1988). Asymptotically efficient prediction of a random field with a misspecified covariance function. Annals of Statistics, 16, 55--63.

[9] Stein, M. L. (1988). An application of the theory of equivalence of Gaussian measures to a prediction problem. IEEE Transactions on Information Theory, 34, 580--582.

[10] Stein, M. L. and Handcock, M. S. (1989). Some asymptotic properties of kriging when the covariance function is misspecified. Mathematical Geology, 21, 171--190.

[11] Stein, M. L. (1989). The loss of efficiency in kriging prediction caused by misspecifications of the covariance structure. Geostatistics, vol. 1, ed. M. Armstrong. Kluwer, Dordrecht, 273--282.

[12] Stein, M. L. (1989). Asymptotic distributions of minimum norm quadratic estimators of the covariance function of a Gaussian random field. Annals of Statistics, 17, 980--1000.

[13] Stein, M. L. (1990). Uniform asymptotic optimality of linear predictions of a random field using an incorrect second-order structure. Annals of Statistics, 18, 850--872.

[14] Stein, M. L. (1990). Bounds on the efficiency of linear predictions using an incorrect covariance function. Annals of Statistics, 18, 1116--1138.

[15] Stein, M. L. (1990). A comparison of generalized cross validation and modified maximum likelihood for estimating the parameters of a stochastic process. Annals of Statistics, 18, 1139--1157.

[16] Stein, M. L. (1991). A kernel approximation to the kriging predictor of a spatial process. Annals of the Institute of Statistical Mathematics, 43, 61--75.

[17] Stein, M. L. (1991). A new class of estimators for the reduced second moment measure of point processes. Biometrika, 78, 281--286.

[18] Niu, X. and Stein, M. L. (1992). Space-time ARMA models for satellite ozone data. Computing Science and Statistics, eds. C. Page and R. LePage. Springer-Verlag, New York, 225--234.

[19] Stein, M. L. (1992). Prediction and inference for truncated spatial data. Journal of Computational and Graphical Statistics, 1, 91--110.

[20] Styer, P. E. and Stein, M. L. (1992). Acid deposition models for detecting the effect of changes in emissions: an exploratory investigation utilizing meteorological variables. Atmospheric Environment, 26A, 3019--3028.

[21] Niu, X., Frederick, J. E., Stein, M. L. and Tiao, G. C. (1992). Trends in column ozone based on TOMS data: Dependence on month, latitude and longitude. Journal of Geophysical Research Atmospheres, 97, D13, 14, 661--14,669.

[22] Stein, M. L. (1992). Estimating the effect of emissions strategies on wet deposition of sulfates. Environmetrics, 3, 235--259.

[23] Zhang, B. and Stein, M. L. (1993). Kernel approximations for universal kriging predictors. Journal of Multivariate Analysis, 44, 286--313.

[24] Handcock, M. S. and Stein, M. L. (1993). A Bayesian analysis of kriging. Technometrics, 35, 403--410.

[25] Stein, M. L. (1993). Spline smoothing with an estimated order parameter. Annals of Statistics, 21, 1522--1544.

[26] Stein, M. L., Shen, X. and Styer, P. (1993). Applications of a simple regression model to acid rain data. Canadian Journal of Statistics, 21, 331--346.

[27] Stein, M. L. (1993). Asymptotically optimal estimation for the reduced second moment measure of point processes. Biometrika, 78, 281--286.

[28] Stein, M. L. (1993). Asymptotic properties of center systematic sampling for predicting integrals of spatial processes. Annals of Applied Probability, 3, 874--880.

[29] Stein, M. L. (1993). A simple condition for asymptotic optimality of linear predictions of random fields. Statistics and Probability Letters, 17, 399--404.

[30] Stein, M. L. (1995). An approach to asymptotic inference for spatial point processes. Statistica Sinica, 5, 221--234.

[31] Stein, M. L. (1995). Predicting integrals of stochastic processes. Annals of Applied Probability, 5, 158--170.

[32] Stein, M. L. (1995). Fixed domain asymptotics for spatial periodograms. Journal of the American Statistical Association, 90, 1277--1288.

[33] Stein, M. L. (1995). Predicting integrals of random fields using observations on a lattice. Annals of Statistics, 23, 1975--1990.

[34] Stein, M. L. (1995). Locally lattice sampling designs for isotropic random fields. Annals of Statistics, 23, 1991--2012.

[35] Floresroux, E. M. and Stein, M. L. (1996). A new method of edge correction for estimating the nearest neighbor distribution. Journal of Statistical Planning and Inference, 50, 353--371.

[36] Stein, M. L. (1997). Efficiency of linear predictors for periodic processes using an incorrect covariance function. Journal of Statistical Planning Inference, 58, 321-331.

[37] Fang, D. and Stein, M. L. (1998). Some statistical methods for analyzing the TOMS data. Journal of Geophysical Research, 103, 26,165--26,182.

[38] Stein, M. L. (1999). Predicting random fields with increasingly dense observations. Annals of Applied Probability, 9, 242--273.

[39] Quashnock, J. M. and Stein, M. L. (1999). A new measure of the clustering of QSO heavy-element absorption-line systems. Astrophysical Journal, 515, 506--511

[40] Stein, M. L. (1999). Inference for point processes based on many short realizations. In Proceedings of the 31st Symposium on the Interface: Models, Predictions, and Computing, eds. K. Berk and M. Pourahmadi. Interface Foundation of North America, Fairfax, VA, 352--360.

[41] Stein, M. L., Quashnock, J. M. and Loh, J. M. (2000). Estimating the K function of a point process with an application to cosmology. Annals of Statistics, 28, 1503--1532.

[42] Stein, M. L. (2001). Local stationarity and simulation of self-affine intrinsic random functions. IEEE Transactions on Information Theory, 47, 1385--1390.

[43] Loh, J. M., Quashnock, J. M. and Stein, M. L. (2001). A measurement of the threedimensional clustering of C IV absorption-line systems on scales of 5 to 300 h-1Mpc. Astrophysical Journal, 560, 606--616.

[44] Stein, M. L. (2002). The screening effect in kriging. Annals of Statistics, 30, 298--323.

[45] Stein, M. L (2002). Fast and exact simulation of fractional Brownian surfaces. Journal of Computational and Graphical Statistics, 11, 587--599.

[46] Choi, D., Tiao, G. C. and Stein, M. L. (2002). A statistical model for latitudinal correlations of satellite data. Journal of Geophysical Research, 107, art. no. 4295.

[47] Lesht, B. M., Stroud, J. R., McCormick, M. J., Fahnensteil, G. L., Stein, M. L., Welty, L. J. and Leshkevich, G. A. (2002). An event-driven phytoplankton bloom in southern Lake Michigan observed by satellite. Geophysical Research Letters, 29, 10. 1029/2001GL013533.

[48] Zhu, Z. and Stein, M. L.(2002). Parameter estimation for fractional Brownian surfaces. Statistica Sinica, 12, 863--883.

[49] Loh, J. M., Stein, M. L. and Quashnock, J. M. (2003). Estimating the large-scale structure of the universe using QSO carbon IV absorbers. Journal of the American Statistical Association, 98, 522--532.

[50] Stein, M. L. (2004). Equivalence of Gaussian measures for some nonstationary random fields. Journal of Statistical Planning and Inference, 123, 1--11.

[51] Welty, L. J. and Stein, M. L. (2004). Modeling phytoplankton: Covariance and variogram model specification for phytoplankton levels in Lake Michigan. In geoENV IV, Geostatistics for Environmental Applications, eds. X. Sanchez-Vila, J. Carrera and J. J. Gomez-Hernandez. Kluwer, Dordrecht, 163--173.

[52] Loh, J. M. and Stein, M. L. (2004). Bootstrapping a spatial point process. Statistica Sinica, 14, 69--101.

[53] Stein, M. L., Chi, Z. and Welty, L. J. (2004). Approximating likelihoods for large spatial datasets. Journal of the Royal Statistical Society, Series B, 66, 275--296.

[54] Jun, M. and Stein, M. L. (2004). Statistical comparison of observed and CMAQ modeled daily sulfate levels. Atmospheric Environment, 38, 4427--4436.

[55] Guillas, S., Stein, M. L., Wuebbles, D. J. and Xia, J. (2004). A statistical evaluation of total ozone trends using a chemical-transport model. Journal of Geophysical Research, 109, D22303, doi: 10.1029/2004JD005049.

[56] Stein, M. L. (2005). Space-time covariance functions. Journal of the American Statistical Association, 100, 310--321.

[57] Stein, M. L. (2005). Statistical methods for regular monitoring data. Journal of the Royal Statistical Society, Series B, 67, 667--687.

[58] Im, H., Stein, M. L. and Kotomarthi, V. R. (2005). A new approach to scenario analysis using simplified chemical transport nmodels. Journal of Geophysical Research, 110, D24205, doi: 10.1029/2005JD006417.

[59] Zhu, Z. and Stein, M. L. (2005). Spatial sampling design for parameter estimation of the covariance function. Journal of Statistical Planning and Inference, 134, 583--603.

[60] Zhu, Z. and Stein, M. L. (2006). Spatial sampling design for prediction with estimated parameters. Journal of Agricultural, Biological and Environmental Statistics, 11, 24--49.

[61] Shao, X. and Stein, M. L.  (2006). Statistical conditional simulation of a multiresolution numerical air quality model. Journal of Geophysical Research, Atmospheres. Vol. 111, D15211, doi:10.1029/2005JD007037.

[62] Vrac, M., Hayhoe, K. and Stein, M. L. (2006). Identification and inter-model comparison of seasonal circulation patterns over North America.  International Journal of Climatology, DOI: 10.1002/joc.1422.

[63] Stein, M. L. (2007). Seasonal variations in the spatial-temporal dependence of total column ozone. Environmetrics, 18, 71--86.

[64] Shao, X., Stein, M. L. and Ching, J. (2007). Statistical comparisons of methods for interpolating the output of a numerical air quality model. Journal of Statistical Planning and Inference, 137, 2277--2293.

[65] Jun, M. and Stein, M. L. (2007). An approach to producing space-time covariance functions on spheres. Technometrics, 49, 468--479.

[66] Im., H. K., Stein, M. L. and Zhu, Z.  (2007). Semiparametric estimation of spectral density with irregular observations.  Journal of the American Statistical Association, 102, 726--735.

[67] Stein, M. L. (2007). Spatial variation of total column ozone on a global scale. Annals of Applied Statistics, I, 191--210.

[68] Vrac, M., Stein, M. L., Hayhoe, K. (2007). Statistical downscaling of precipitation through a nonhomogeneous stochastic weather typing approach. Journal of Climate Research, 34, 169--184.

[69] Zhang, Z., Beletsky, D., Schwab, D. J. and Stein, M. L. (2007). Assimilation of current measurements into a circulation model of Lake Michigan. Water Resources Research, 43, Art. No. W11407.

[70] Vrac, M., Stein, M. L., Hayhoe, K. and Liang, X. Z. (2007). A general method for validating statistical downscaling methods under future climate change. Geophysical Research Letters, 34, Art. No. L18701.

[71] Stein, M. L. (2007). A modeling approach for large spatial datasets. Journal of the Korean Statistical Society, 37/1, pp. 3--10, doi:10.1016/j.jkss.2007.09.001.

[72] Jun, M. and Stein, M. L. (2008). Nonstationary covariance models for global data. Annals of Applied Statistics, 2, 1271--1289.

[73] Anderes, E. B. and Stein, M. L.(2008). Estimating deformations of isotropic Gaussian random fields on the plane. Annals of Statistics, 36, 719--741.

[74] Loh, J. M. and Stein, M. L. (2008). Spatial bootstrap with increasing observations in a fixed domain. Statistica Sinica, 18, 667--688.

[75] Lim, C. and Stein, M. L. (2008). Asymptotic properties of spatial cross-periodograms using fixed-domain asymptotics. Journal of Multivariate Analysis, 99, 1962--1984.

[76] Stroud, J., Lesht, B., Schwab, D., Beletsky, D. and Stein, M. L. (2009). Assiimilation of satellite images into a sediment transport model of Lake Michigan.Water Resources Research, 45, W02419.

[77] Stein, M. L. (2009). Spatial interpolation of high frequency monitoring data. Annals of Applied Statistics, 3, 272--291.

[78] Lim, C., Stein, M. L., Ching, J. and Tang, R. (2010). Statistical properties of differences between low and high resolution CMAQ runs with matched initial and boundary conditions. Environmental Modeling & Software, 25, 158--169.

[79] Stroud, J. R., Stein, M. L., Lesht, B. M., Schwab, D. J. and Beletsky, D. (2010). An ensemble Kalman filter and smoother for satellite data assimilation. Journal of the American Statistical Association, 105, 978--990.

[80] Ma, L., Stein, M. L., Wang, M., Shelton, A. O., Pfister, C. A., and Wilder, K. J. (2011). A method for unbiased estimation of population abundance along curvy margins. Environmetrics, 22, 330--339, doi:10.1002/env.1053.

[81] Anderes, E. and Stein, M. L. (2011). Local likelihood estimation for nonstationary random fields. Journal of Multivariate Analysis, 102, 506--520.

[82] Stein, M. L. (2011). When does the screening effect hold? Annals of Statistics, 39, 2795--2819.

[83] Hitczenko, M. and Stein, M. L. (2012). Some theory for anisotropic processes on the sphere. Statistical Methodology, 9, 211--227.

[84] Stein, M. L., Chen, J. and Anitescu, M. (2012). Difference filter preconditioning for large covariance matrices. SIAM Journal on Matrix Analysis and Applications, 33, 52--72.

[85] Stein, M. L. (2012). Simulation of Gaussian random fields with one derivative. Journal of Computational and Graphical Statistics, 21, 155--173.

[86] Stein, M. L., Chen, J. and Anitescu, M. (2013). Stochastic approximation of score functions for Gaussian processes. Annals of Applied Statistics, 7, 1162--1191.

[87] Castruccio, S. and Stein, M. L. (2013). Global space-time models for climate ensembles. Annals of Applied Statistics, 7, 1593--1611.

[88] Guinness, J. and Stein, M. L. (2013). Interpolation of nonstationary high frequency spatial-temporal temperature data. Annals of Applied Statistics, 7, 1684--1708.

[89] Guinness, J. and Stein, M. L. (2013). Transformation to approximate independence for locally stationary Gaussian processes. Journal of Time Series Analysis, 34, 574--590.

[90] Stein, M. L. (2013). Statistical properties of covariance tapers. Journal of Computational and Graphical Statistics, 22, 866--885.

[91] Chang, X. and Stein, M. L. (2013). Decorrelation property of discrete wavelet transform under fixed-domain asymptotics. IEEE Transactions on Information Theory, 59, 8001--8013.

[92] Stein, M. L. (2013). On a class of space-time intrinsic random functions. Bernoulli, 19, 387--408.

[93] Stein, M. L. (2014). Limitations on low rank approximations for covariance matrices of spatial data. Spatial Statistics, 8, 1--19.

[94] Castruccio, S., McInerney, D. J., Stein, M. L., Crouch, F., Jacob, R. L. and Moyer, E. J. (2014). Statistical emulation of climate model projections based on precomputed GCM runs. Journal of Climate, 27, 1829--1844.

[95] Poppick, A. and Stein, M. L. (2014). Using covariates to model dependence innonstationary, high-frequencymeteorological processes. Environmetrics, 25, 293--305.

[96] Chang, X. and Stein, M. L. (2014). Wavelet methods in interpolation of high-frequency spatial-temporal pressure. Spatial Statistics, 8, 52--68.

[97] Leeds, W. B., Moyer, E. J., Stein, M. L. (2015). Simulation of future climate under changing temporal covariance structures. Advances in Statistical Climatology, Meteorology and Oceanography, 1, 1--14.

[98] Stein, M. L. (2015). When does the screening effect not hold? Spatial Statistics, 11, 65--80.

[99] Horrell, M. T. and Stein, M. L. (2015). A covariance parameter estimation method for polar-orbiting satellite data. Statistica Sinica, 25, 41--59.

[100] Wang, J., Swati, Stein, M. L. and Kotamarthi, V. R. (2015). Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model. Journal of Geophysical Research Atmospheres, 120, 1239--1259.

[101] Sun, Y. and Stein, M. L. (2015). A stochastic space-time model for intermittent precipitation occurrences. Annals of Applied Statistics, 9, 2110--2132.

[102] Sun, Y. and Stein, M. L. (2016). Statistically and computationally efficient estimating equations for large spatial datasets. Journal of Computational and Graphical Statistics, 25, 187--208.

[103] Wang, J., Han, Y., Stein, M. L., Kotamarthi, V. R., Huang, W. K. (2016). Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model. Climate Dynamics, doi:10.1007/s00382-016-3000-3.

[104] Poppick, A., McInerney, D. J., Moyer, E. J., Stein, M. L. (2016). Temperatures in transient climates: improved methods for simulations with evolving temporal covariances. Annals of Applied Statistics, 10, 477--505.

[105] Huang, W. K., Stein, M. L., McInerney, D. J., Sun, S., Moyer, E. J. (2016). Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions. Advances in Statistical Climatology, Meteorology and Oceanograpy, 2, 79--103.

Discussions and reviews:

[1] Stein, M. L. (1989). Discussion of “Space-time modelling with long-memory dependence: assessing Irelands wind power resource,” by J. Haslett and A. Raftery. Applied Statistics, 38, 39.

[2] Stein, M. L. (1989). Review of Statistical Analysis of Spherical Data by N. I. Fisher, T. Lewis, and B. J. J. Embleton. Technometrics, 31, 393--394.

[3] Stein, M. L. (1990). Discussion of “Design and Analysis of Computer Experiments,” by J. Sacks, W. J. Welch, T. J., and H. P. Wynn. Statistical Science, 4, 432--433.

[4] Stein, M. L. (1990). Review of Estimating and Choosing, by G. Matheron. Technometrics, 32, 358--359.

[5] Stein, M. L. (1991). Review of The Analysis of Directional Time Series: Applications to Wind Speed and Direction, by J. Breckling. Technometrics, 33, 485--486.

[6] Stein, M. L. and Fang, D. (1997). Discussion of “Ozone exposure and population density in Harris County, Texas,” by R. J. Carroll, et al. Journal of the American Statistical Association, 92, 408--411.

[7] Stein, M. L. (1998). Discussion of “Model-based geostatistics,” by P.J. Diggle, J.A. Tawn and R.A. Moyeed. Applied Statistics, 47, 341--342.

[8] Stein, M. L. (1998). Discussion of “The kriged Kalman filter,” by K. V. Mardia, et al. Test, 7, 272--276.

[9] Stein, M. L. (1999). Discussion of “Prediction of spatial cummulative distribution functions using subsampling,” by S. N. Lahiri, et al. Journal of the American Statistical Association, 94, 106--107.

[10] Stein, M. L. (2000). Review of Geostatistics: Modeling Spatial Uncertainty, by J.-P. Chilès and P. Delfiner. Journal of the American Statistical Association, 95, 335--337.

[11] Fuentes, M., Guttorp, P. and Stein, M. L. (2008). Special section on statistics in the atmospheric sciences. Annals of Applied Statistics, 2, 1143--1147.

[12] Loredo, T. J., Rice, J. and Stein, M. L. (2009). Introduction to papers on astrostatistics. Annals of Applied Statistics, 3, 1--5.

[13] Stein, M. L. (2010). Discussion of “Geostatistical inference under preferential sampling,” by P.J. Diggle, R. Menezes and T. Su. Applied Statistics, 59, 223.

[14] Stein, M. L. (2011). Editorial. Annals of Applied Statistics, 5, 1--4.

[15] Katz, R. W., Craigmile, P. F.,Guttorp, P., Haran, M., Sansó, B. and Stein, M. L. (2013). Uncertainty analysis in climate change assessments. Nature Climate Change, 3, 769--771.

 

Last update: 1/25/14

 

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