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Recent Advances in Functional Data Analysis and Related Topics
New technologies allow us to handle increasingly large datasets, while monitoring devices are becoming ever more sophisticated. This high-tech progress produces statistical units sampled over finer and finer grids. As the measurement points become closer, the data can be considered as observations v...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Heidelberg :
Physica-Verlag HD : Imprint: Physica,
2011.
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| Edición: | 1st ed. 2011. |
| Colección: | Contributions to Statistics
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 024 | 7 | |a 10.1007/978-3-7908-2736-1 |2 doi | |
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| 245 | 1 | 0 | |a Recent Advances in Functional Data Analysis and Related Topics |h [electronic resource] / |c edited by Frédéric Ferraty. |
| 250 | |a 1st ed. 2011. | ||
| 264 | 1 | |a Heidelberg : |b Physica-Verlag HD : |b Imprint: Physica, |c 2011. | |
| 300 | |a XVIII, 322 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Contributions to Statistics | |
| 505 | 0 | |a Penalized Spline Approaches for Functional Principal Component Logit -- Functional Prediction for the Residual Demand in Electricity Spot -- Variable Selection in Semi-Functional RegressionModels -- Power Analysis for Functional Change Point Detection -- Robust Nonparametric Estimation for Functional Spatial Regression -- Sequential Stability Procedures for Functional Data Setups -- On the Effect of Noisy Observations of the Regressor in a Functional Linear Model -- Testing the Equality of Covariance Operators -- Modeling and Forecasting Monotone Curves by FDA -- Wavelet-Based Minimum Contrast Estimation of Linear Gaussian Random Fields -- Dimensionality Reduction for Samples of Bivariate Density Level Sets: an Application to Electoral Results -- Structural Tests in Regression on Functional Variable -- A Fast Functional Locally Modeled Conditional Density and Mode for Functional Time-Series -- Generalized Additive Models for Functional Data -- Recent Advances on Functional Additive Regression -- Thresholding in Nonparametric Functional Regression with Scalar Response -- Estimation of a Functional Single Index Model -- Density Estimation for Spatial-Temporal Data -- Functional Quantiles -- Extremality for Functional Data -- Functional Kernel Estimators of Conditional Extreme Quantiles -- A Nonparametric Functional Method for Signature Recognition -- Longitudinal Functional Principal Component Analysis -- Estimation and Testing for Geostatistical Functional Data -- Structured Penalties for Generalized Functional Linear Models (GFLM) -- Consistency of the Mean and the Principal Components of Spatially Distributed Functional Data -- Kernel Density Gradient Estimate -- A Backward Generalization of PCA for Exploration and Feature Extraction of Manifold-Valued Shapes -- Multiple Functional Regression with both Discrete and Continuous Covariates -- Factor Modeling for High Dimensional Time Series -- Depth for Sparse Functional Data -- Sparse Functional Linear Regression with Applications to Personalized Medicine -- Estimation of Functional Coefficients in Partial Differential Equations -- Functional Varying Coefficient Models -- Applications of Functional Data Analysis to Material Science -- On the Properties of Functional Dept -- Second-Order Inference for Functional Data with Application to DNA Minicircles -- Nonparametric Functional Time Series Prediction -- Wavelets Smoothing for Multidimensional Curves -- Nonparametric Conditional Density Estimation for Functional Data. Econometric Applications. | |
| 520 | |a New technologies allow us to handle increasingly large datasets, while monitoring devices are becoming ever more sophisticated. This high-tech progress produces statistical units sampled over finer and finer grids. As the measurement points become closer, the data can be considered as observations varying over a continuum. This intrinsic continuous data (called functional data) can be found in various fields of science, including biomechanics, chemometrics, econometrics, environmetrics, geophysics, medicine, etc. The failure of standard multivariate statistics to analyze such functional data has led the statistical community to develop appropriate statistical methodologies, called Functional Data Analysis (FDA). Today, FDA is certainly one of the most motivating and popular statistical topics due to its impact on crucial societal issues (health, environment, etc). This is why the FDA statistical community is rapidly growing, as are the statistical developments . Therefore, it is necessary to organize regular meetings in order to provide a state-of-art review of the recent advances in this fascinating area. This book collects selected and extended papers presented at the second International Workshop of Functional and Operatorial Statistics (Santander, Spain, 16-18 June, 2011), in which many outstanding experts on FDA will present the most relevant advances in this pioneering statistical area. Undoubtedly, these proceedings will be an essential resource for academic researchers, master students, engineers, and practitioners not only in statistics but also in numerous related fields of application. . | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Probabilities. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Molecular genetics. | |
| 650 | 0 | |a Atmospheric science. | |
| 650 | 1 | 4 | |a Statistics. |
| 650 | 2 | 4 | |a Probability Theory. |
| 650 | 2 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Molecular Genetics. |
| 650 | 2 | 4 | |a Atmospheric Science. |
| 700 | 1 | |a Ferraty, Frédéric. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783790828337 |
| 776 | 0 | 8 | |i Printed edition: |z 9783790827378 |
| 776 | 0 | 8 | |i Printed edition: |z 9783790827354 |
| 830 | 0 | |a Contributions to Statistics | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-7908-2736-1 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||