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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 online resource (246 p.) |
| Soggetto topico |
Humanities
Social interaction |
| Soggetto non controllato |
abdominal aortic aneurysm
ant colony system asymptotic theory bayesian spatial mixture model causal and dilated convolutional neural networks deep learning DWD EEG/MEG data elastic net emulation ensembling entropy-based robust EM estimation consistency feature fusion feature representation financial time series generalized linear models high dimension high dimensional predictors high dimensional time-series high-dimensional high-dimensional data information complexity criteria inverse problem L2-consistency Lasso Medicare data missingness mechanism mixture regression model selection multicategory classification nonlocal prior nonparamteric boostrap nuisance penalty methods post-selection inference prediction proximal algorithm random subspaces regularization segmentation sparse group lasso sparse PCA stepwise regression strong selection consistency text mining trend analysis unconventional likelihood |
| 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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