參考文獻 |
Altman, N. (2000), “Krige, Smooth, Both or Neither?” (with discussion), Aus tralian & New Zealand Journal of Statistics, 42, 441-461.
Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in Proceedings of the Second International Symposium on Information Theory, eds. Petro, B. and Csa ́ki, F., Budapest: Akade ́miai Kiado ́, pp. 267–281.
Bookstein, F. L. (1989), “Principal Warps: Thin Plate Splines and the Decomposition of Deformations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 6, 567–585.
Bradley, J. R., Cressie, N., and Shi, T. (2012), “Local Spatial-Predictor Selection,” Proceedings of the Joint Statistical Meetings, Alexandria, VA. Section on Statistics and the Environment: 3098 - 3110.
Bradley, J. R., Cressie, N., and Shi, T. (2011), “Selection of Rank and Basis Functions in the Spatial Random Effects Model,” in Proceedings of the Joint Statistical Meetings, 3393–3406. Alexandria, VA: American Statistical Association.
Burnham, K. P. and Anderson, D. R. (2002), Model Selection and Multimodel Inference: A Practical Informationtheoretic Approach (2nd ed.), New York: Springer.
Chen, C. S. and Chen, C. S. (2018), “A Composite Spatial Predictor via Local Criteria under a Misspecified Model,” Stoch Environ Res Risk Assess, 32, 341–355.
Chen, C. S. and Huang, H. C. (2011), “Geostatistical Model Averaging Based on Conditional Information Criteria,” Environmental and Ecological Statistics, 19, 23–35.
Craven, P. and Wahba, G. (1979), “Smoothing Noisy Data with Spline Functions: Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-Validation,” Numerische Mathematik, 31, 377–403.
Cressie, N. (1993), Statistics for Spatial Data, Revised edition: Wiley, New York.
Cressie, N. and Lahiri, S. N. (1996), “Asymptotics for REML Estimation of Spatial Covariance Parameters,” Journal of Statistical Planningand Inference, 50, 327–341.
Cressie, N., and Johannesson, G. (2008), “Fixed RankKriging for very Large Spatial Data Sets,” Journal of the Royal Statistical Society, Series B, 70, 209–226.
Duchon, J. (1976), “Splines Minimizing Rotation Invariant Semi-norms in Sobolev Spaces,” Constructive theory of functions of several variables, Springer-Verlag, Berlin, pp 85–100.
Furrer, R., Genton, M. G. and Nychka, D. (2006), “Covariance Tapering for Interpolation of Large Spatial Datasets,” J Comput Graph Stat, 15, 502–523.
Green, P. J. and Silverman, B. W. (1993), Nonparametric Regression and Generalized LinearModels: A Roughness Penalty Approach, BocaRaton, FL: CRC Press.
Huang, H. C. and Chen, C. S. (2007), “Optimal Geostatistical Model Selection,” Journal of the American Statistical Association, 102, 1009–1024.
Hurvich, C. M. and Tsai, C. L. (1989), “Regression and Time Series Model Selection in Small Samples,” Biometrika, 76, 297–307.
Hutchinson, M. F. and de Hoog, F. R. (1985), “Smoothing Noisy Data with Spline Functions,” Numerische Mathematik, 47, 99–106.
Laslett, G. M. (1994), “Kriging and Splines: An Empirical Comparison of Their Predictive Performance in Some Applications,” Journal of the American Statistical Association, 89, 391-409.
Kaufman, C. G., Schervish, M. J. and Nychka, D. W. (2008), “Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets,” J Am Stat Assoc, 103, 1545–1555.
Kinniburgh, D. and Smedley, P. (2001), “Arsenic Contamination of Groundwater in Bangladesh,” Technical report, British Geological Survey.
Mate ́rn, B. (1986), Spatial Variation (2nd ed.), New York: Springer.
McGilchrist, C. A. (1989), “Bias of ML and REML Estimators in Regression Models with ARMA Errors,” Journal of Statistical Computation and Simu lation, 32, 127-136.
Micchelli, C. A. (1986), “Interpolation of Scattered Data: DistanceMatrices and Conditionally Positive Definite Functions,” Constructive Approximation, 2, 11–22.
Patterson, H. D and Thompson, R. (1971), “Recovery of Inter-Block Information when Block Sizes Are Unequal,” Biometrika, 58:545–554.
Roy, P. K. and Hossain, S. S. (2014), “Predicting Arsenic Concentration in Groundwater of Bangladesh Using Bayesian Geostatistical Model,” Environmental and Ecological Statistics, 21, 583–597.
Schabenberger, O. and Gotway, C. A. (2005), Statistical Methods for Spatial Data Analysis, Boca Raton, FL: Chapman & Hall/CRC.
Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6,461-464.
Tzeng, S. and Huang, H. C. (2018), “Resolution Adaptive Fixed Rank Kriging,” Technometrics, 60, 198-208.
Vaida, F. and Blanchard, S. (2005), “Conditional Akaike Information for Mixed-Effects Models,” Biometrika, 92, 351 – 370.
Wahba, G. (1990), Spline Models for Observational Data, Philadelphia: Society for Industrial and Applied Mathematics.
Wahba, G., and Wendelberger, J. (1980), “Some New Mathematical Methods for Variational Objective Analysis using Splines and Cross Validation,” MonthlyWeather Review, 108, 1122–1143.
Wang, Y (1998), “Smoothing Spline Models with Correlated Random Errors,” Journal of the American Statistical Association, 93, 341-348. |