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1. |
Record Nr. |
UNISA996495167003316 |
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Autore |
Mathai Arak M |
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Titolo |
Multivariate Statistical Analysis in the Real and Complex Domains [[electronic resource] /] / by Arak M. Mathai, Serge B. Provost, Hans J. Haubold |
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Pubbl/distr/stampa |
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Cham, : Springer Nature, 2022 |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (XXVII, 921 p. 3 illus.) |
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Disciplina |
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Soggetti |
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Mathematical statistics |
Statistics |
Multivariate analysis |
System theory |
Mathematical Statistics |
Statistical Theory and Methods |
Multivariate Analysis |
Complex Systems |
Anàlisi multivariable |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis. |
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Sommario/riassunto |
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This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. |
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2. |
Record Nr. |
UNINA9910733713503321 |
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Autore |
Chaudhuri Arijit <1940-> |
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Titolo |
A Comprehensive Textbook on Sample Surveys / / by Arijit Chaudhuri, Sanghamitra Pal |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (xxiv, 257 pages) |
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Collana |
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Indian Statistical Institute Series, , 2523-3122 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Meaning and purpose of Survey Sampling -- 2. Inference in Survey Sampling -- 3. Sampling with Varying Probabilities -- 4. Fixing the size of an Equal Probability Sample -- 5. Adjusting Unit-Nonresponse by weighting & Tackling Item-Nonresponse by Imputation -- 6. Randomized Response & Indirect Survey Techniques -- 7. Super-Population Modeling. Model-Assisted Approach. Asymptotics -- 8. Prediction Approach: Robustness, Bayesian Methods, Empirical Bayes -- 9. Small Area Estimation & Developing Small Domain Statistics -- 10. |
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Estimation of non-linear Parametric functions -- 11. Permanent random numbers, Poisson Sampling and Collocated Sampling -- 12. Network and Adaptive Sampling -- 13. Fixing size of a sample in Complex Strategies -- 14. Inadequate and Multiple Frame Data and Conditional Inference -- 15. Study of Analytical Surveys -- An Epilogue -- References. . |
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Sommario/riassunto |
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As a comprehensive textbook in survey sampling, this book discusses the inadequacies of classic, designed-based inferential procedures and provides alternative approaches in the form of model formulations, model-design-based procedures of analysis, inference and interpretation. The book focuses on a wide range of topics which included Bayesian and Empirical Bayesian approaches, complex procedures of stratification, clustering, sampling in multi stages and phases, linear and non-linear estimation of parameters, small area estimation by spatial and chronological modelling, network and adaptive sampling methods and more. The book includes detailed case studies and exercises, making it valuable for students of statistics, specifically survey sampling. . |
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