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Applied multivariate statistical analysis / Wolfgang Karl Hardle, Léopold Simar
| Applied multivariate statistical analysis / Wolfgang Karl Hardle, Léopold Simar |
| Autore | Härdle, Wolfgang Karl |
| Edizione | [4. ed] |
| Pubbl/distr/stampa | Berlin ; Heidelberg, : Springer, 2015 |
| Descrizione fisica | XIII, 580 p. : ill. ; 24 cm |
| Altri autori (Persone) | Simar, Léopold |
| Soggetto topico |
62H12 - Estimation in multivariate analysis [MSC 2020]
62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62H17 - Contingency tables [MSC 2020] 62H15 - Hypothesis testing in multivariate analysis [MSC 2020] 62H20 - Measures of association (correlation, canonical correlation, etc.) [MSC 2020] 62H10 - Multivariate distribution of statistics [MSC 2020] 62F25 - Parametric tolerance and confidence regions [MSC 2020] |
| Soggetto non controllato |
Cluster analysis
Conjoint Measurement Analysis Discriminant Analysis Elastic Net Hypothesis Testing Lasso Multivariate Analysis Projection Persuit Quantitative Finance Sliced Inverse Regression |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0113928 |
Härdle, Wolfgang Karl
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| Berlin ; Heidelberg, : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Applied multivariate statistical analysis / Wolfgang Karl Hardle, Léopold Simar
| Applied multivariate statistical analysis / Wolfgang Karl Hardle, Léopold Simar |
| Autore | Härdle, Wolfgang K. |
| Edizione | [4. ed] |
| Pubbl/distr/stampa | Berlin ; Heidelberg, : Springer, 2015 |
| Descrizione fisica | XIII, 580 p. : ill. ; 24 cm |
| Altri autori (Persone) | Simar, Léopold |
| Soggetto topico |
62F25 - Parametric tolerance and confidence regions [MSC 2020]
62H10 - Multivariate distribution of statistics [MSC 2020] 62H12 - Estimation in multivariate analysis [MSC 2020] 62H15 - Hypothesis testing in multivariate analysis [MSC 2020] 62H17 - Contingency tables [MSC 2020] 62H20 - Measures of association (correlation, canonical correlation, etc.) [MSC 2020] 62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] |
| Soggetto non controllato |
Cluster analysis
Conjoint Measurement Analysis Discriminant Analysis Elastic Net Hypothesis Testing Lasso Multivariate Analysis Projection Persuit Quantitative Finance Sliced Inverse Regression |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00113928 |
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Härdle, Wolfgang K.
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| Berlin ; Heidelberg, : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications
| Big Data Analytics and Information Science for Business and Biomedical Applications |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (246 p.) |
| Soggetto topico |
Humanities
Social interaction |
| Soggetto non controllato |
high-dimensional
nonlocal prior strong selection consistency estimation consistency generalized linear models high dimensional predictors model selection stepwise regression deep learning financial time series causal and dilated convolutional neural networks nuisance post-selection inference missingness mechanism regularization asymptotic theory unconventional likelihood high dimensional time-series segmentation mixture regression sparse PCA entropy-based robust EM information complexity criteria high dimension multicategory classification DWD sparse group lasso L2-consistency proximal algorithm abdominal aortic aneurysm emulation Medicare data ensembling high-dimensional data Lasso elastic net penalty methods prediction random subspaces ant colony system bayesian spatial mixture model inverse problem nonparamteric boostrap EEG/MEG data feature representation feature fusion trend analysis text mining |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557614803321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer
| Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer |
| Autore | Lederer, Johannes |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xiv, 355 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H22 - Probabilistic graphical models [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Calibration
Estimation Graphical Models High dimensional inference High-Dimensional Data High-dimensional statistics Lasso Linear regression Prediction R labs Regularization Sparsity Statistical inference |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0277448 |
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Lederer, Johannes
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer
| Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / Johannes Lederer |
| Autore | Lederer, Johannes |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xiv, 355 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H22 - Probabilistic graphical models [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Calibration
Estimation Graphical Models High dimensional inference High-Dimensional Data High-dimensional statistics Lasso Linear regression Prediction R labs Regularization Sparsity Statistical inference |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00277448 |
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Lederer, Johannes
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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