Big and Complex Data Analysis (Record no. 427192)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04976nam a22005535i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOKNUMBER | |
| International Standard Book Number | 9783319415734 |
| -- | 978-3-319-41573-4 |
| 024 7# - | |
| -- | 10.1007/978-3-319-41573-4 |
| -- | doi |
| 040 ## - | |
| -- | ISI Library, Kolkata |
| 050 #4 - | |
| -- | QA276-280 |
| 072 #7 - | |
| -- | PBT |
| -- | bicssc |
| 072 #7 - | |
| -- | MAT029000 |
| -- | bisacsh |
| 072 #7 - | |
| -- | PBT |
| -- | thema |
| 082 04 - DEWEYDECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.5 |
| Edition number | 23 |
| 245 10 - TITLE STATEMENT | |
| Title | Big and Complex Data Analysis |
| Medium | [electronic resource] : |
| Remainder of title | Methodologies and Applications / |
| Statement of responsibility, etc | edited by S. Ejaz Ahmed. |
| 942 ## - ADDED ENTRY ELEMENTS(KOHA) | |
| Koha item type | E-BOOKS |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE STATEMENTS | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing : |
| -- | Imprint: Springer, |
| Date of production, publication, distribution, manufacture | 2017. |
| 300 ## - | |
| -- | XIV, 386 p. 85 illus., 55 illus. in color. |
| -- | online resource. |
| 336 ## - CONTENT TYPE | |
| Content Type Term | text |
| Content Type Code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media Type Term | computer |
| Media Type Code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier Type Term | online resource |
| Carrier Type Code | cr |
| Source | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - | |
| -- | Contributions to Statistics, |
| -- | 1431-1968 |
| 505 0# - | |
| -- | Preface -- Introduction -- Unsupervised Bump Hunting Using Principal Components -- Statistical Process Control Charts as a Tool for Analyzing Big Data -- Empirical Likelihood Test for High Dimensional Generalized Linear Models -- Identifying gene-environment interactions associated with prognosis using penalized quantile regression -- A Computationally Efficient Approach for Modeling Complex and Big Survival Data -- Regularization after marginal learning for ultra-high dimensional regression models -- Tests of concentration for low-dimensional and high-dimensional directional data -- Random Projections For Large-Scale Regression -- How Different are Estimated Genetic Networks of Cancer Subtypes? -- Analysis of correlated data with error-prone response under generalized linear mixed models -- High-Dimensional Classification for Brain Decoding -- Optimal shrinkage estimation in heteroscedastic hierarchical linear models -- Bias-reduced moment estimators of Population Spectral Distribution and their applications -- Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values -- A Mixture of Variance-Gamma Factor Analyzers -- Fast Community Detection in Complex Networks with a K-Depths Classifier. |
| 520 ## - | |
| -- | This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers. |
| 650 #0 - | |
| -- | Mathematical statistics. |
| 650 #0 - | |
| -- | Big data. |
| 650 #0 - | |
| -- | Statistical methods. |
| 650 #0 - | |
| -- | Data mining. |
| 650 14 - | |
| -- | Statistical Theory and Methods. |
| -- | http://scigraph.springernature.com/things/product-market-codes/S11001 |
| 650 24 - | |
| -- | Statistics and Computing/Statistics Programs. |
| -- | http://scigraph.springernature.com/things/product-market-codes/S12008 |
| 650 24 - | |
| -- | Big Data/Analytics. |
| -- | http://scigraph.springernature.com/things/product-market-codes/522070 |
| 650 24 - | |
| -- | Biostatistics. |
| -- | http://scigraph.springernature.com/things/product-market-codes/L15020 |
| 650 24 - | |
| -- | Data Mining and Knowledge Discovery. |
| -- | http://scigraph.springernature.com/things/product-market-codes/I18030 |
| 700 1# - | |
| -- | Ahmed, S. Ejaz. |
| -- | editor. |
| -- | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - | |
| -- | SpringerLink (Online service) |
| 773 0# - | |
| -- | Springer eBooks |
| 776 08 - | |
| -- | Printed edition: |
| -- | 9783319415727 |
| 776 08 - | |
| -- | Printed edition: |
| -- | 9783319415741 |
| 776 08 - | |
| -- | Printed edition: |
| -- | 9783319823874 |
| 830 #0 - | |
| -- | Contributions to Statistics, |
| -- | 1431-1968 |
| 856 40 - | |
| -- | https://doi.org/10.1007/978-3-319-41573-4 |
| 912 ## - | |
| -- | ZDB-2-SMA |
| 950 ## - | |
| -- | Mathematics and Statistics (Springer-11649) |
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