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Functional and Operatorial Statistics
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Heidelberg :
Physica-Verlag HD : Imprint: Physica,
2008.
|
| Edición: | 1st ed. 2008. |
| Colección: | Contributions to Statistics
|
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-7908-2062-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220117025625.0 | ||
| 007 | cr nn 008mamaa | ||
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| 024 | 7 | |a 10.1007/978-3-7908-2062-1 |2 doi | |
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| 245 | 1 | 0 | |a Functional and Operatorial Statistics |h [electronic resource] / |c edited by Sophie Dabo-Niang, Frédéric Ferraty. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Heidelberg : |b Physica-Verlag HD : |b Imprint: Physica, |c 2008. | |
| 300 | |a XX, 304 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 | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Contributions to Statistics | |
| 505 | 0 | |a to IWFOS'2008 -- Solving Multicollinearity in Functional Multinomial Logit Models for Nominal and Ordinal Responses -- Estimation of Functional Regression Models for Functional Responses by Wavelet Approximation -- Functional Linear Regression with Functional Response: Application to Prediction of Electricity Consumption -- Asymptotic Normality of Robust Nonparametric Estimator for Functional Dependent Data -- Measuring Dissimilarity Between Curves by Means of Their Granulometric Size Distributions -- Supervised Classification for Functional Data: A Theoretical Remark and Some Numerical Comparisons -- Local Linear Regression for Functional Predictor and Scalar Response -- Spatio-temporal Functional Regression on Paleoecological Data -- Local Linear Functional Regression Based on Weighted Distance-based Regression -- Singular Value Decomposition of Large Random Matrices (for Two-Way Classification of Microarrays) -- On Tensorial Products of Hilbertian Linear Processes -- Recent Results on Random and Spectral Measures with Some Applications in Statistics -- Parameter Cascading for High Dimensional Models -- Advances in Human Protein Interactome Inference -- Functional Principal Components Analysis with Survey Data -- Functional Clustering of Longitudinal Data -- Robust Nonparametric Estimation for Functional Data -- Estimation of the Functional Linear Regression with Smoothing Splines -- A Random Functional Depth -- Parametric Families of Probability Distributions for Functional Data Using Quasi-Arithmetic Means with Archimedean Generators -- Point-wise Kriging for Spatial Prediction of Functional Data -- Nonparametric Regression on Functional Variable and Structural Tests -- Vector Integration and Stochastic Integration in Banach Spaces -- Multivariate Functional Data Discrimination Using ICA: Analysis of Hippocampal Differences in Alzheimer's Disease -- Influence in the Functional Linear Model with Scalar Response -- Is it Always Optimal to Impose Constraints on Nonparametric Functional Estimators? Some Evidence on the Smoothing Parameter Choice -- Dynamic Semiparametric Factor Models in Pricing Kernels Estimation -- The Operator Trigonometry in Statistics -- Selecting and Ordering Components in Functional-Data Linear Prediction -- Bagplots, Boxplots and Outlier Detection for Functional Data -- Marketing Applications of Functional Data Analysis -- Nonparametric Estimation in Functional Linear Model -- Presmoothing in Functional Linear Regression -- Probability Density Functions of the Empirical Wavelet Coefficients of Multidimensional Poisson Intensities -- A Cokriging Method for Spatial Functional Data with Applications in Oceanology -- On the Effect of Curve Alignment and Functional PCA -- K-sample Subsampling -- Inference for Stationary Processes Using Banded Covariance Matrices -- Automatic Local Spectral Envelope -- Recent Advances in the Use of SVM for Functional Data Classification -- Wavelet Thresholding Methods Applied to Testing Significance Differences Between Autoregressive Hilbertian Processes -- Explorative Functional Data Analysis for 3D-geometries of the Inner Carotid Artery -- Inference on Periodograms of Infinite Dimensional Discrete Time Periodically Correlated Processes. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Probabilities. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Biometry. | |
| 650 | 1 | 4 | |a Probability Theory. |
| 650 | 2 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Quantitative Economics. |
| 650 | 2 | 4 | |a Biostatistics. |
| 700 | 1 | |a Dabo-Niang, Sophie. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 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 9783790823042 |
| 776 | 0 | 8 | |i Printed edition: |z 9783790825602 |
| 776 | 0 | 8 | |i Printed edition: |z 9783790820614 |
| 830 | 0 | |a Contributions to Statistics | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-7908-2062-1 |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) | ||