
Analysis of Neural Data by Robert E Kass
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data.-
The Elements of Statistical Learning
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Dragons of Winter Night
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Functional Data Analysis
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Time Series: Theory and Methods
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Modeling Discrete Time-to-Event Data
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Targeted Learning
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Models for Discrete Longitudinal Data
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Regression Modeling Strategies
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Targeted Learning in Data Science
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An Introduction to Sequential Monte Carlo
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Modern Multidimensional Scaling
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Correlation Theory of Stationary and Related Random Functions
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Mathematical Statistics
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Breakthroughs in Statistics
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Annotated Readings in the History of Statistics
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Statistical Models Based on Counting Processes
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Hidden Markov Processes and Adaptive Filtering
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Design of Observational Studies
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Chaos: A Statistical Perspective
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A Comparison of the Bayesian and Frequentist Approaches to Estimation
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Smoothing Spline ANOVA Models
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Finite Mixture and Markov Switching Models
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The Gini Methodology
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Ten Projects in Applied Statistics
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Growth Curve Models and Statistical Diagnostics
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Statistical Methods in Software Engineering
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Markov Bases in Algebraic Statistics
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Data
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A Course on Point Processes
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Shrinkage Estimation
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Theory of Statistics
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Bayesian and Frequentist Regression Methods
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A Statistical Model
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Prediction Theory for Finite Populations
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Tools for Statistical Inference
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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ARMA Model Identification
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Statistical Design and Analysis for Intercropping Experiments
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Approximate Distributions of Order Statistics
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Robust Asymptotic Statistics
Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).
Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.
Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.
| SKU | Unavailable |
| ISBN 13 | 9781461496014 |
| ISBN 10 | 1461496012 |
| Title | Analysis of Neural Data |
| Author | Robert E Kass |
| Series | Springer Series In Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2014-03-04 |
| Number of pages | 648 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |








































