Functional and Operatorial StatisticsSophie Dabo-Niang, Frédéric Ferraty Springer Science & Business Media, 2008 M05 21 - 304 pages An increasing number of statistical problems and methods involve infinite-dimensional aspects. This is due to the progress of technologies which allow us to store more and more information while modern instruments are able to collect data much more effectively due to their increasingly sophisticated design. This evolution directly concerns statisticians, who have to propose new methodologies while taking into account such high-dimensional data (e.g. continuous processes, functional data, etc.). The numerous applications (micro-arrays, paleo- ecological data, radar waveforms, spectrometric curves, speech recognition, continuous time series, 3-D images, etc.) in various fields (biology, econometrics, environmetrics, the food industry, medical sciences, paper industry, etc.) make researching this statistical topic very worthwhile. This book gathers important contributions on the functional and operatorial statistics fields. |
Contents
| 1 | |
| 10 | |
| 13 | |
| 15 | |
5 | 26 |
2 | 31 |
3 | 33 |
References | 40 |
Influence in the Functional Linear Model with Scalar | 164 |
49 | 171 |
50 | 187 |
The Operator Trigonometry in Statistics | 189 |
Selecting and Ordering Components in FunctionalData | 194 |
79 | 200 |
Bagplots Boxplots and Outlier Detection for Functional | 201 |
Marketing Applications of Functional Data Analysis | 209 |
Scalar Response | 47 |
2 | 54 |
Recent Results on Random and Spectral Measures with | 77 |
Parameter Cascading for High Dimensional Models David Campbell Jiguo Cao Giles Hooker and James Ramsay | 84 |
Advances in Human Protein Interactome Inference | 89 |
Functional Principal Components Analysis with Survey | 95 |
Functional Clustering of Longitudinal Data | 103 |
Estimation of the Functional Linear Regression with | 117 |
A Random Functional Depth | 121 |
Parametric Families of Probability Distributions | 127 |
Pointwise Kriging for Spatial Prediction of Functional | 134 |
Nonparametric Regression on Functional Variable | 143 |
References | 144 |
Vector Integration and Stochastic Integration in Banach | 151 |
Presmoothing in Functional Linear Regression | 223 |
81 | 236 |
A Cokriging Method for Spatial Functional Data with | 237 |
54 | 242 |
On the Effect of Curve Alignment and Functional PCA | 243 |
Inference for Stationary Processes Using Banded | 255 |
Automatic Local Spectral Envelope 263 | 262 |
82 | 266 |
Wavelet Thresholding Methods Applied to Testing | 281 |
85 | 286 |
Explorative Functional Data Analysis for 3Dgeometries | 289 |
Discrete Time Periodically Correlated Processes | 296 |
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Common terms and phrases
applications approach approximation assume assumptions asymptotic bagplot Banach spaces basis functions Cardot classification coefficients compute consider constraint convergence covariance operator cross-validation data set defined denote density derivatives dimensional distance-based distribution e-mail eigenfunctions eigenvalues Ferraty and Vieu finite Fraiman func Functional Data Analysis functional linear model functional linear regression functional predictor functional principal component functional regression functional variables given Gustafson Hilbert space hippocampal hippocampi kernel estimator L2 distance linear regression logit matrix measure methods multivariate Nonparametric estimation Nonparametric Functional nonparametric regression observed obtained operator trigonometry orthonormal outliers prediction principal component analysis problem procedures proposed QAMML Quasi-Arithmetic Mean Ramsay and Silverman random function random variable regression model resp robust sample curves Sarda scalar response semi-metric Sobolev space splines Springer Statistics structure Theorem tion Toulouse values vector wavelet
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