Forecasting functional time series
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aguilera, 2006, Using principal components for estimating logistic regression with high-dimensional multicollinear data, Computational Statistics & Data Analysis, 50, 1905, 10.1016/j.csda.2005.03.011
Alho, 2005, Remarks on the use of probabilities in demography and forecasting, 27
Alho, 2005
Bellman, 1961
Cai, 2006, Prediction in functional linear regression, Annals of Statistics, 34, 2159, 10.1214/009053606000000830
Chatfield, 1993, Calculating interval forecasts, Journal of Business & Economic Statistics, 11, 121, 10.2307/1391361
Chatfield, 2000
Dauxois, 1982, Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference, Journal of Multivariate Analysis, 12, 136, 10.1016/0047-259X(82)90088-4
Delaigle, A., Hall, P., & Apanasovich, T. V. (2009). Weighted least squares methods for prediction in the functional data linear model. arXiv:0902.3319v1 [stat.ME]. http://arxiv.org/abs/0902.3319v1
Erbas, 2007, Forecasting age-specific breast cancer mortality using functional data models, Statistics in Medicine, 26, 458, 10.1002/sim.2306
Escabias, 2004, Principal component estimation of function logistic regression: Discussion of two different approaches, Journal of Nonparametric Statistics, 16, 365, 10.1080/10485250310001624738
Faber, 2002, Uncertainty estimation for multivariate regression coefficients, Chemometrics and Intelligent Laboratory Systems, 64, 169, 10.1016/S0169-7439(02)00102-8
Fernández Pierna, 2003, Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error, Chemometrics and Intelligent Laboratory Systems, 65, 281, 10.1016/S0169-7439(02)00139-9
Greenshtein, 2006, Best subset selection, persistence in high-dimensional statistical learning and optimization under l1 constraint, Annals of Statistics, 34, 2367, 10.1214/009053606000000768
Greenshtein, 2004, Persistence in high-dimensional linear predictor selection and the virtue of overparameterization, Bernoulli, 10, 971, 10.3150/bj/1106314846
Hall, 2007, Methodology and convergence rates for functional linear regression, Annals of Statistics, 35, 70, 10.1214/009053606000000957
Hall, 2006, On properties of functional principal components analysis, Journal of the Royal Statistical Society: Series B, 68, 109, 10.1111/j.1467-9868.2005.00535.x
Hall, 2009, Theory for high-order bounds in functional principal components analysis, Mathematical Proceedings of the Cambridge Philosophical Society, 146, 225, 10.1017/S0305004108001850
Hall, 2006, Properties of principal component methods for functional and longitudinal data analysis, Annals of Statistics, 34, 1493, 10.1214/009053606000000272
Human Mortality Database, (2008). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Data downloaded on 10 Sep 2008 www.mortality.org
Hyndman, R. J. (2007). addb: Australian Demographic Data Bank. R package version 3.222. http://www.robhyndman.info/Rlibrary/addb
Hyndman, 2008, Stochastic population forecasts using functional data models for mortality, fertility and migration, International Journal of Forecasting, 24, 323, 10.1016/j.ijforecast.2008.02.009
Hyndman, 2008
Hyndman, R. J., & Shang, H. L. (2008a). ftsa: Functional time series analysis. R package version 1.0. http://monashforecasting.com/index.php?title=R_packages
Hyndman, R. J., & Shang, H. L. (2008b). Rainbow plots, bagplots, and boxplots for functional data. Working paper 9/08, Department of Econometrics & Business Statistics, Monash University. http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2008/9-08.php
Hyndman, 2007, Robust forecasting of mortality and fertility rates: A functional data approach, Computational Statistics & Data Analysis, 51, 4942, 10.1016/j.csda.2006.07.028
Kargin, 2008, Curve forecasting by functional autoregression, Journal of Multivariate Analysis, 99, 2508, 10.1016/j.jmva.2008.03.001
Krämer, 2008, Penalized partial least squares with applications to B-spline transformations and functional data, Chemometrics and Intelligent Laboratory Systems, 94, 60, 10.1016/j.chemolab.2008.06.009
Martens, 1989
Mesle, 1991, Reconstitution of annual life tables for nineteenth-century France, Population: An English Selection, 3, 33
Ng, P. T., & Maechler, M. (2008). COBs: COBS — Constrained B-splines (Sparse matrix based). R package version 1.1-5. http://wiki.r-project.org/rwiki/doku.php?id=packages:cran:cobs
Preda, 2005, Clusterwise PLS regression on a stochastic process, Computational Statistics & Data Analysis, 49, 99, 10.1016/j.csda.2004.05.002
Preda, 2005, PLS regression on a stochastic process, Computational Statistics & Data Analysis, 48, 149, 10.1016/j.csda.2003.10.003
Ramsay, 1991, Some tools for functional data analysis (with discussion), Journal of the Royal Statistical Society: Series B, 53, 539
Ramsay, 2002
Ramsay, 2005
Reiss, 2007, Functional principal component regression and functional partial least squares, Journal of the American Statistical Association, 102, 984, 10.1198/016214507000000527
Rice, 1991, Estimating the mean and covariance structure nonparametrically when the data are curves, Journal of the Royal Statistical Society: Series B, 53, 233
Silverman, 1995, Incorporating parametric effects into functional principal components analysis, Journal of the Royal Statistical Society: Series B, 57, 673
Silverman, 1996, Smoothed functional principal components analysis by choice of norm, Annals of Statistics, 24, 1, 10.1214/aos/1033066196
Wedderburn, 1974, Quasi-likelihood functions, generalized linear models, and the Gauss–Newton method, Biometrika, 61, 439
Wilmoth, 2007
Wold, 1975, Soft modelling by latent variables: The non-linear iterative partial least squares (NIPALS) approach, 117
Wood, 1994, Monotonic smoothing splines fitted by cross validation, SIAM Journal on Scientific Computing, 15, 1126, 10.1137/0915069