
Analysis of Multivariate and High-Dimensional Data by Inge Koch
'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.-
Data Analysis and Graphics Using R
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Measure Theory and Filtering
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Essentials of Statistical Inference
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Markov Chains
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Applied Asymptotics
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A User's Guide to Measure Theoretic Probability
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Probability
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High-Dimensional Probability
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Asymptotic Statistics
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Statistical Models
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Model Selection and Model Averaging
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Bootstrap Methods and their Application
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Random Graph Dynamics
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Modern Statistical Methods and Theory
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A Course in Interacting Particle Systems
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Networks
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The Coordinate-Free Approach to Linear Models
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The Estimation and Tracking of Frequency
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Semiparametric Regression
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Statistical Mechanics of Disordered Systems
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Random Networks for Communication
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Statistical Analysis of Stochastic Processes in Time
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Regression for Categorical Data
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Numerical Methods of Statistics
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Symmetry Studies
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Nonparametric Estimation under Shape Constraints
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Elements of Distribution Theory
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From Finite Sample to Asymptotic Methods in Statistics
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Bayesian Methods
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Design of Comparative Experiments
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Mathematical Foundations of Infinite-Dimensional Statistical Models
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Probability on Trees and Networks
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Spectral Analysis for Univariate Time Series
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Predictive Statistics
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Long-Range Dependence and Self-Similarity
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Statistical Hypothesis Testing in Context: Volume 52
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Saddlepoint Approximations with Applications
'… this book is suitable for readers with various backgrounds and interests and can be read at different levels… [It] will also be useful for working statisticians who are interested in analysis of multivariate or high-dimensional data.' Yasunori Fujikoshi, Mathematical Reviews
'A major advantage and important feature of this book is that it illustrates the interconnection of various techniques, such as the connection between discriminant analysis and principal component analysis, cluster analysis and principal component analysis, and factor analysis and cluster analysis. The author has undoubtedly taken on an appreciable task of collecting, compiling, connecting and presenting classical as well as recent developments concisely … Readers are expected to have an adequate background in statistics to understand the topics in this book. This well-designed book is suitable for advanced multivariate courses where the emphasis is more on applications. Application-orientated researchers will also find this book useful. Problems for each chapter are given at the end of each section. Graphics are used at various places to facilitate the understanding of topics. The book is highly recommended for libraries.' Journal of the Royal Statistical Society
'I must highly commend the author for writing an excellent comprehensive review of multivariate and high dimensional statistics … The lucid treatment and thoughtful presentation are two additional attractive features … Without any hesitation and with admiration, I would give the author a 10 out of 10 … The feat she has accomplished successfully for this difficult area of statistics is something very few could accomplish. The wealth of information is enormous and a motivated student can learn a great deal from this book … I highly recommend [it] to researchers working in the field of high dimensional data and to motivated graduate students.' Ravindra Khattree, International Statistical Review
'A major advantage and important feature of this book is that it illustrates the interconnection of various techniques, such as the connection between discriminant analysis and principal component analysis, cluster analysis and principal component analysis, and factor analysis and cluster analysis. The author has undoubtedly taken on an appreciable task of collecting, compiling, connecting and presenting classical as well as recent developments concisely … Readers are expected to have an adequate background in statistics to understand the topics in this book. This well-designed book is suitable for advanced multivariate courses where the emphasis is more on applications. Application-orientated researchers will also find this book useful. Problems for each chapter are given at the end of each section. Graphics are used at various places to facilitate the understanding of topics. The book is highly recommended for libraries.' Journal of the Royal Statistical Society
'I must highly commend the author for writing an excellent comprehensive review of multivariate and high dimensional statistics … The lucid treatment and thoughtful presentation are two additional attractive features … Without any hesitation and with admiration, I would give the author a 10 out of 10 … The feat she has accomplished successfully for this difficult area of statistics is something very few could accomplish. The wealth of information is enormous and a motivated student can learn a great deal from this book … I highly recommend [it] to researchers working in the field of high dimensional data and to motivated graduate students.' Ravindra Khattree, International Statistical Review
Inge Koch is Associate Professor of Statistics at the University of Adelaide, Australia.
| SKU | Unavailable |
| ISBN 13 | 9780521887939 |
| ISBN 10 | 0521887933 |
| Title | Analysis of Multivariate and High-Dimensional Data |
| Author | Inge Koch |
| Series | Cambridge Series In Statistical And Probabilistic Mathematics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Cambridge University Press |
| Year published | 2013-12-02 |
| Number of pages | 526 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |




































