[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-1 Mpc. 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] Porcu, E. and Stein, M. L. (2012). On some local, global and
regularity behaviour of some classes of covariance functions.
In: Porcu E., Montero J., Schlather M. (eds), Advances and Challenges in
Space-time Modelling of Natural Events. Lecture Notes in Statistics, vol
207. Springer, Berlin.
[87] Stein, M. L., Chen, J. and Anitescu, M.
(2013).
Stochastic approximation of score functions for Gaussian processes.
Annals of Applied Statistics, 7,
1162-1191.
[88] Castruccio, S. and Stein, M. L.
(2013).
Global space-time models for climate ensembles.
Annals of Applied Statistics, 7, 1593-1611.
[89] Guinness, J. and Stein, M. L. (2013).
Interpolation of nonstationary high frequency spatial-temporal temperature
data. Annals of Applied Statistics, 7,
1684-1708.
[90] Guinness, J. and Stein, M. L. (2013).
Transformation to approximate independence for locally stationary Gaussian
processes.
Journal of Time Series Analysis, 34, 574-590.
[91] Stein, M. L. (2013). Statistical properties of covariance tapers.
Journal of Computational and Graphical Statistics, 22, 866-885.
[92] 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.
[93] Stein, M. L. (2013).
On a class of space-time intrinsic random functions.
Bernoulli, 19, 387-408.
[94] Stein, M. L. (2014).
Limitations on low rank approximations for covariance matrices of
spatial data.
Spatial Statistics, 8, 1-19.
[95] 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.
[96] Poppick, A. and Stein, M. L. (2014). Using covariates to model dependence
innonstationary, high-frequencymeteorological processes.
Environmetrics, 25, 293-305.
[97] Chang, X. and Stein, M. L. (2014).
Wavelet methods in interpolation of high-frequency spatial-temporal pressure.
Spatial Statistics, 8, 52-68.
[98] 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.
[99] Stein, M. L. (2015). When does the screening effect not hold?
Spatial Statistics, 11, 65-80.
[100] Horrell, M. T. and Stein, M. L. (2015).
A covariance parameter estimation method
for polar-orbiting satellite data. Statistica Sinica, 25,
41-59.
[101] 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.
[102] Sun, Y. and Stein, M. L. (2015). A stochastic space-time model for
intermittent precipitation occurrences. Annals of Applied Statistics,
9, 2110-2132.
[103] 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.
[104] 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.
[105] 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.
[106] 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.
[107] Bao, J., McInerney, D. J. and Stein, M. L. (2016). A spatial-dependent model
for climate emulation. Environmetrics, 27, 396-408.
[108] Chang, W., Stein, M. L., Wang, J., Kotamarthi, V. R. and Moyer, E. J. (2016).
Changes in spatio-temporal precipitation patterns in changing
climate conditions. Journal of Climate, 29, 8355-8376.
[109]
Anitescu, M., Chen, J. and Stein, M. L. (2017). An inversion-free estimating
equations approach for Gaussian process models. Journal of
Computational and Graphical Statistics, 26, 98-107.
[110]
Stein, M. L. (2017). Should annual maximum temperatures follow a generalized
extreme value distribution?
Biometrika, 104, 1-16.
[111]
Xu, W. and Stein, M. L. (2017). Maximum likelihood estimation for a smooth
Gaussian random field model.
SIAM/ASA Journal on Uncertainty Quantification, 5, 138-175.
[112]
Poppick, A., Moyer, E. J. and Stein, M. L. (2017). Estimating trends in the
global mean temperature record.
Advances in Statistical Climatology, Meteorology and Oceanography,
3, 33-53.
[113]
Stroud, J. R., Stein, M. L. and Lysen, S. (2017). Bayesian and maximum
likelihood estimation for Gaussian processes on an incomplete lattice.
Journal of Computational and Graphical Statistics, 26,
108-120.
[114]
Horrell, M. T. and Stein, M. L. (2017). Half-spectral space-time covariance
models. Spatial Statistics, 19, 90-100.
[115]
Haugen, M. A., Stein, M. L. Moyer, E. J. Moyer and Sriver, R. L. (2018).
Estimating changes in temperature distributions in a large ensemble of climate
simulations using quantile regression. Journal of Climate,
31, 8573-8588.
[116]
Baugh, S. and Stein, M. L. (2018).
Computationally efficient spatial modeling using recursive skeletonization
factorizations. Spatial Statistics, 27, 18-30.
[117]
Kuusela, M. and Stein, M. L. (2018).
Locally stationary spatio-temporal interpolation of Argo profiling float data.
Proceedings of the Royal Sociey A, 474, 20180400.
[118]
Xu, W., Stein, M. L. and Wisher, I. (2019).
Modeling and predicting chaotic circuit data. Journal of Uncertainty
Quantification, 7, 31-52.
[119]
Haugen, M. A., Stein, M. L., Sriver, R. L. and Moyer, E. J. (2019).
Future climate emulations using quantile regressions on large ensembles.
Advances in Statistical Climatology, Meteorology and Oceanography,
5, 37-55.
[120]
Geoga, C. J., Anitescu, M. and Stein, M. L. (2020).
Scalable Gaussian process computations using hierarchical matrices.
Journal of Computational and Graphical Statistics,
29 227-237.
[121]
Stein, M. L. (2020). Some statistical issues in climate science.
Statistical Science, 35, 31-41.
[122]
Stein, M. L. (2021). Parametric models for distributions when interest
is in extremes with an application to daily temperature. Extremes,
24, 293-323.
https://doi.org/10.1007/s10687-020-00378-z.
[123]
Stein, M. L. (2020).
A parametric model for distributions with flexible behavior in both tails.
Environmetrics, 2020;e2658. https://doi.org/10.1002/env.2658
[124]
Yuan, J., Stein, M. L. and Kopp, R. E. (2020).
The evolving distribution of relative humidity conditional upon daily maximum
temperature in a warming climate.
Journal of Geophysical Research: Atmospheres, 125,
e2019JD032100. https://doi.org/10.1029/2019JD032100
[125]
Geoga, C. J., Anitescu, M. and Stein, M. L. (2021). Flexible nonstationary
spatiotemporal modeling of high-frequency monitoring data.
Environmetrics, 2021;e2670. https://doi.org/10.1002/env.2670
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.
[16]
Stein, M. L. and Hung, Y. (2019). Comment on ``Probabilistic integration: A
role in statistical computation?'' by F.-X. Briol, et al. Statistical
Science, 34, 34--37.
