Applying Generalized Linear Models by James K Lindsey
Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference.-
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A Modern Approach to Regression with R
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All of Statistics
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Time Series Analysis and Its Applications
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A Modern Introduction to Probability and Statistics
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Modern Mathematical Statistics with Applications
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Introduction to Time Series and Forecasting
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Statistics and Data Analysis for Financial Engineering
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Probability with Applications in Engineering, Science, and Technology
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Bayesian Essentials with R
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A First Course in Bayesian Statistical Methods
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Monte Carlo Statistical Methods
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The Bayesian Choice
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Plane Answers to Complex Questions
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Fundamentals of High-Dimensional Statistics
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Log-Linear Models and Logistic Regression
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Basics of Modern Mathematical Statistics
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Statistical Learning from a Regression Perspective
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Pathologie des Thymus
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Applied Regression Analysis
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Lectures on Advanced Topics in Categorical Data Analysis
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Counting for Something
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Regression Analysis
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Statistical Methods: The Geometric Approach
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Applied Multivariate Data Analysis
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Probability
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Introduction to Statistics
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Studying Human Populations
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Introduction to Statistical Inference
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Elements of Statistics for the Life and Social Sciences
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Analysis of Variance in Experimental Design
From a review:
TECHNOMETRICS
"..one of the best applied statistical books that I have ever read."
| SKU | Unavailable |
| ISBN 13 | 9780387982182 |
| ISBN 10 | 0387982183 |
| Title | Applying Generalized Linear Models |
| Author | James K Lindsey |
| Series | Springer Texts In Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 1997-06-20 |
| Number of pages | 256 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |