Mathematical and statistical software Books

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Book SynopsisFor researchers who use surveys interested in learning how to seize vast possibilities and flexibility of R in survey analysis/visualizations. Psychologists, marketeers, HR personnel, managers, other professionals who wish to standardize/automate the process for visualizing survey data. Suitable for textbook courses.Table of ContentsI Preparation. 1. Survey data. 2. Process. 3. Variables. 4. Categories. 5. Read data. 6. Parse values. 7. Validate data. 8. Pre-process data. 9. Build a dataset. 10. Basic statistics. 11. Create plots with ggplot2. 12. Save plots to files. 13. R Markdown. II Plotting. 14. Numeric plots. 15. Bar charts. 16. Percentage bars. 17. Diverging percentage bars. 18. Pie charts. 19. Lollipop plots. 20. Dot plots. 21. Heatmaps. 22. Geographic maps. 23. Missing value plots. 24. Validation plots.

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Book SynopsisIn this book, various numerical methods are discussed in a comprehensive way. It delivers a mixture of theory, examples and MATLAB practicing exercises to help the students in improving their skills. To understand the MATLAB programming in a friendly style, the examples are solved. The MATLAB codes are mentioned in the end of each topic. Throughout the text, a balance between theory, examples and programming is maintained.Key Features Methods are explained with examples and codes System of equations has given full consideration Use of MATLAB is learnt for every method This book is suitable for graduate students in mathematics, computer science and engineering.Table of Contents1. Common Commands Used in Matlab. 2. System of Linear Equations. 3. Polynomial Interpolation. 4. Root Finding Methods. 5. Numerical Integration. 6. Solution of Initial Value Problems. 7. Boundary Value Problems.

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Book SynopsisStatistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing.The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphiTrade Review"This book follows a well established approach to the psychometric analysis of questionnaire data as found in educational, survey and medical research. The authors provide an in-depth discussion of the analysis of score reliability and item properties grounded in classical test theory (CTT), and of the probabilistic modeling of individual responses based on latent variable models. … Chapter 5 is a bit different and focus on the estimation of item and person parameters and the diagnostic of IRT models. The first part is rather technical but it does a good job at describing Statistical Analysis of Questionnaires the pros and cons of each technique–joint, conditional and marginal maximum likelihood–and how they could be implemented using custom software. … The authors conclude (…) by highlighting multidimensional IRT models which allow to relax the strong hypothesis of unidimensionality that is attached to all previous models, as well as the main strengths of structural equation models which can be viewed as providing the glue between factor analytic methods and IRT. Overall, the authors succeed at presenting a solid and reliable framework for psychometric analysis of questionnaire data."— Christophe Lalanne, Paris-Diderot University, in the Journal of Statistical Software, November 2017Table of ContentsPreliminaries. Classical Test Theory. Item Response Theory Models for Dichotomous Items. Item Response Theory Models for Polytomous Items. Estimation Methods and Diagnostics. Some Extensions of Traditional Item Response Theory Models.

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Book SynopsisForecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called ADAM (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analTable of Contents1. Introduction 2. Forecasts evaluation 3. Time series components and simple forecasting methods 4. Introduction to ETS 5. Pure additive ADAM ETS 6. Pure multiplicative ADAM ETS 7. General ADAM ETS model 8. Introduction to ARIMA 9. ADAM ARIMA 10. Explanatory variables in ADAM 11. Estimation of ADAM 12. Multiple frequencies in ADAM 13. Intermittent State Space Model 14. Model diagnostics 15. Model selection and combinations in ADAM 16. Handling uncertainty in ADAM 17. Scale model for ADAM 18. Forecasting with ADAM 19. Forecasting functions of the smooth package 20. What’s next?

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Book SynopsisCompositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.Trade Review"(…This book) avoids cumbersome theoretical digressions and only presents to the reader the essential basic concepts for the application of CODA, using ratios and logratios that retain most of the original data structure and, subsequently, may lead to proper conclusions. … The simplification of the analysis and the straightforward interpretability of results is, clearly, one of the primary values of the publication. In addition, the emphasis on the general application of weights in the calculus of most of the operations and methodologies used throughout the book deserves a special mention.. … Altogether, the book and the easyCODA R package may represent a promising instrument for introducing CODA in the fat and oils field, where fatty acid compositions have been treated until now exclusively by classical multivariate techniques without considering their compositional structure. Predicting the future is risky, but the book may represent an essential instrument for CODA spreading since it represents just what many practitioners were expecting to initiate their experience in this promising new statistical field of compositional data analysis."—A. Garrido Fernández in Gracas y Aceites – International Journal of Fats and Oils, July-September 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, Department of Earth and Environmental Sciences, University of WaterlooWaterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, University of Waterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019Table of ContentsWhat are compositional data, and why are they special? Geometry and visualization of compositional data. Logratio transformations. Properties and distributions of logratios. Regression models involving compositional data. Dimension reduction using logratio analysis. Clustering of compositional data. The problem of zeros, with some solutions. Simplifying the task: variable selection. Case study: Fatty acids of marine amphipods. Appendix A: Theory of compositional data analysis. Appendix B: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: Epilogue.

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Book SynopsisAfter the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a websTrade Review"This book is a good tool for self-learning analytical strategies for omics data. It requires previous knowledge of R and focuses on getting things done...I think the book would be a good reference for masters or PhD students that have to perform their analysis and need a starting point. Also, for the practicing statistician working with omics data."- Victor Moreno, ISCB News, July 2020 Table of Contents1 Introduction 2 Case examples 3 Dealing with omic data in Bioconductor 4 Genetic association studies 5 Genomic variant studies 6 Adressing batch effects 7 Transcriptomic studies 8 Epigenomic studies 9 Exposomic analysis 10 Enrichment analysis 11 Multiomic data analysis

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Book SynopsisHands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algoTrade Review"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"...The book describes in detail the various methods for solving classification and clustering problems. Functions from many R libraries are compared, which enables the reader to understand their respective advantages and disadvantages. The authors have developed a clear structure to the book that includes a brief description of each model, examples of using the model for specific real-life examples, and discussion of the advantages and disadvantages of the model. This structure is one of the book’s main advantages."- Igor Malyk, ISCB News, July 2020Table of ContentsI FUNDAMENTALS 1. Introduction to Machine Learning 1.1 Supervised learning 1.1.1 Regression problems 1.1.2 Classification problems 1.2 Unsupervised learning 1.3 Roadmap 1.4 The data sets 2. Modeling Process 2.1 Prerequisites 2.2 Data splitting 2.2.1 Simple random sampling 2.2.2 Stratified sampling 2.2.3 Class imbalances 2.3 Creating models in R 2.3.1 Many formula interfaces 2.3.2 Many engines 2.4 Resampling methods 2.4.1 k-fold cross validation 2.4.2 Bootstrapping 2.4.3 Alternatives 2.5 Bias variance trade-off 2.5.1 Bias 2.5.2 Variance 2.5.3 Hyperparameter tuning 2.6 Model evaluation 2.6.1 Regression models 2.6.2 Classification models 2.7 Putting the processes together 3. Feature & Target Engineering 3.1 Prerequisites 3.2 Target engineering 3.3 Dealing with missingness 3.3.1 Visualizing missing values 3.3.2 Imputation 3.4 Feature filtering 3.5 Numeric feature engineering 3.5.1 Skewness 3.5.2 Standardization 3.6 Categorical feature engineering 3.6.1 Lumping 3.6.2 One-hot & dummy encoding 3.6.3 Label encoding 3.6.4 Alternatives 3.7 Dimension reduction 3.8 Proper implementation 3.8.1 Sequential steps 3.8.2 Data leakage 3.8.3 Putting the process together II SUPERVISED LEARNING 4. Linear Regression 4.1 Prerequisites 4.2 Simple linear regression 4.2.1 Estimation 4.2.2 Inference 4.3 Multiple linear regression 4.4 Assessing model accuracy 4.5 Model concerns 4.6 Principal component regression 4.7 Partial least squares 4.8 Feature interpretation 4.9 Final thoughts 5. Logistic Regression 5.1 Prerequisites 5.2 Why logistic regression 5.3 Simple logistic regression 5.4 Multiple logistic regression 5.5 Assessing model accuracy 5.6 Model concerns 5.7 Feature interpretation 5.8 Final thoughts 6. Regularized Regression 6.1 Prerequisites 6.2 Why regularize? 6.2.1 Ridge penalty 6.2.2 Lasso penalty 6.2.3 Elastic nets 6.3 Implementation 6.4 Tuning 6.5 Feature interpretation 6.6 Attrition data 6.7 Final thoughts 7. Multivariate Adaptive Regression Splines 7.1 Prerequisites 7.2 The basic idea 7.2.1 Multivariate regression splines 7.3 Fitting a basic MARS model 7.4 Tuning 7.5 Feature interpretation 7.6 Attrition data 7.7 Final thoughts 8. K-Nearest Neighbors 8.1 Prerequisites 8.2 Measuring similarity 8.2.1 Distance measures 8.2.2 Pre-processing 8.3 Choosing k 8.4 MNIST example 8.5 Final thoughts 9 Decision Trees 9.1 Prerequisites 9.2 Structure 9.3 Partitioning 9.4 How deep? 9.4.1 Early stopping 9.4.2 Pruning 9.5 Ames housing example 9.6 Feature interpretation 9.7 Final thoughts 10. Bagging 10.1 Prerequisites 10.2 Why and when bagging works 10.3 Implementation 10.4 Easily parallelize 10.5 Feature interpretation 10.6 Final thoughts 11. Random Forests 11.1 Prerequisites 11.2 Extending bagging 11.3 Out-of-the-box performance 11.4 Hyperparameters 11.4.1 Number of trees 11.4.2 mtry 11.4.3 Tree complexity 11.4.4 Sampling scheme 11.4.5 Split rule 11.5 Tuning strategies 11.6 Feature interpretation 11.7 Final thoughts 12. Gradient Boosting 12.1 Prerequisites 12.2 How boosting works 12.2.1 A sequential ensemble approach 12.2.2 Gradient descent 12.3 Basic GBM 12.3.1 Hyperparameters 12.3.2 Implementation 12.3.3 General tuning strategy 12.4 Stochastic GBMs 12.4.1 Stochastic hyperparameters 12.4.2 Implementation 12.5 XGBoost 12.5.1 XGBoost hyperparameters 12.5.2 Tuning strategy 12.6 Feature interpretation 12.7 Final thoughts 13. Deep Learning 13.1 Prerequisites 13.2 Why deep learning 13.3 Feedforward DNNs 13.4 Network architecture 13.4.1 Layers and nodes 13.4.2 Activation 13.5 Backpropagation 13.6 Model training 13.7 Model tuning 13.7.1 Model capacity 13.7.2 Batch normalization 13.7.3 Regularization 13.7.4 Adjust learning rate 13.8 Grid Search 13.9 Final thoughts 14. Support Vector Machines 14.1 Prerequisites 14.2 Optimal separating hyperplanes 14.2.1 The hard margin classifier 14.2.2 The soft margin classifier 14.3 The support vector machine 14.3.1 More than two classes 14.3.2 Support vector regression 14.4 Job attrition example 14.4.1 Class weights 14.4.2 Class probabilities 14.5 Feature interpretation 14.6 Final thoughts 15. Stacked Models 15.1 Prerequisites 15.2 The Idea 15.2.1 Common ensemble methods 15.2.2 Super learner algorithm 15.2.3 Available packages 15.3 Stacking existing models 15.4 Stacking a grid search 15.5 Automated machine learning 15.6 Final thoughts 16. Interpretable Machine Learning 16.1 Prerequisites 16.2 The idea 16.2.1 Global interpretation 16.2.2 Local interpretation 16.2.3 Model-specific vs. model-agnostic 16.3 Permutation-based feature importance 16.3.1 Concept 16.3.2 Implementation 16.4 Partial dependence 16.4.1 Concept 16.4.2 Implementation 16.4.3 Alternative uses 16.5 Individual conditional expectation 16.5.1 Concept 16.5.2 Implementation 16.6 Feature interactions 16.6.1 Concept 16.6.2 Implementation 16.6.3 Alternatives 16.7 Local interpretable model-agnostic explanations 16.7.1 Concept 16.7.2 Implementation 16.7.3 Tuning 16.7.4 Alternative uses 16.8 Shapley values 16.8.1 Concept 16.8.2 Implementation 16.8.3 XGBoost and built-in Shapley values 16.9 Localized step-wise procedure 16.9.1 Concept 16.9.2 Implementation 16.10Final thoughts III DIMENSION REDUCTION 17. Principal Components Analysis 17.1 Prerequisites 17.2 The idea 17.3 Finding principal components 17.4 Performing PCA in R 17.5 Selecting the number of principal components 17.5.1 Eigenvalue criterion 17.5.2 Proportion of variance explained criterion 17.5.3 Scree plot criterion 17.6 Final thoughts 18. Generalized Low Rank Models 18.1 Prerequisites 18.2 The idea 18.3 Finding the lower ranks 18.3.1 Alternating minimization 18.3.2 Loss functions 18.3.3 Regularization 18.3.4 Selecting k 18.4 Fitting GLRMs in R 18.4.1 Basic GLRM model 18.4.2 Tuning to optimize for unseen data 18.5 Final thoughts 19. Autoencoders 19.1 Prerequisites 19.2 Undercomplete autoencoders 19.2.1 Comparing PCA to an autoencoder 19.2.2 Stacked autoencoders 19.2.3 Visualizing the reconstruction 19.3 Sparse autoencoders 19.4 Denoising autoencoders 19.5 Anomaly detection 19.6 Final thoughts IV Clustering 20. K-means Clustering 20.1 Prerequisites 20.2 Distance measures 20.3 Defining clusters 20.4 k-means algorithm 20.5 Clustering digits 20.6 How many clusters? 20.7 Clustering with mixed data 20.8 Alternative partitioning methods 20.9 Final thoughts 21. Hierarchical Clustering 21.1 Prerequisites 21.2 Hierarchical clustering algorithms 21.3 Hierarchical clustering in R 21.3.1 Agglomerative hierarchical clustering 21.3.2 Divisive hierarchical clustering 21.4 Determining optimal clusters 21.5 Working with dendrograms 21.6 Final thoughts 22. Model-based Clustering 22.1 Prerequisites 22.2 Measuring probability and uncertainty 22.3 Covariance types 22.4 Model selection 22.5 My basket example 22.6 Final thoughts Bibliography Index

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Book SynopsisDesign and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to: Make an appropriate design choice based on the objectives of a research project Create a design and perform an experiment Interpret the results of computer data analysis The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is uTrade Review"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"Thank you for writing your phenomenal book "Design and Analysis of Experiments with R". I'm teaching a new course this spring on experimental design and reinforcement learning. The students are graduate bioengineers, so I was having difficulty finding a text that blends theory, practice, and computation. Your book excels at all three. The first chapter I read clarified several topics and improved both my teaching and research. After testing a dozen DOE and RSM books, yours is the clear winner. I understand the enormous time that goes into a well-constructed textbook. I hope this message conveys my deep appreciation for your effort."—Paul Jensen, Ph.D., Assistant Professor , Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"In my opinion, this is a very valuable book. It covers the topics that I judge should be in such a book including what might be called the standard designs and more … it has become my go to text on experimental design."David E. Booth, TechnometricsTable of ContentsIntroduction. Completely Randomized Designs with One Factor. Factorial Designs. Randomized Block Designs. Designs to Study Variances. Fractional Factorial Designs. Incomplete and Confounded Block Designs. Split-Plot Designs. Crossover and Repeated Measures Designs. Response Surface Designs. Mixture Experiments. Robust Parameter Design Experiments. Experimental Strategies for Increasing Knowledge. Bibliography. Index.

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Book SynopsisCombinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics. This definitive reference/user's guide provides examples of all 450 Combinatorica functions in action, along with tutorial text on the mathematical and algorithmic theory.Trade ReviewReview of the hardback: 'This book is the definite reference guide to Combinatorica … it is more than just a reference since it has all the necessary theory to comprehend the concepts … It is a very readable edition full of graphical and stimulating approaches to combinatorics and graph theories … This is a great resource for the acknowledgment of beautiful patterns and important properties of graphs and other combinatorial objects … This book is highly recommended. it is well organized, and readable textbook for beginners and intermediate students.' Leonardo On-lineTable of Contents1. Combinatorica: an explorer's guide; 2. Permutations and combinations; 3. Algebraic combinatorics; 4. Partitions, compositions and Young tableaux; 5. Graph representation; 6. Generating graphs; 7. Properties of graphs; 8. Algorithmic graph theory.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis concise text, first published in 2003, is for a one-semester course for upper-level undergraduates and beginning graduate students in engineering, science, and mathematics, and can also serve as a quick reference for professionals. The treatment of each method is brief and technical issues are minimized, but all the issues important in practice and for understanding the code are discussed.Trade Review' … this is a readable, accessible text full of invaluable advice, illustrated using interesting examples and exercises … if you do have some background knowledge of numerical analysis, MATLAB, and are motivated by the application of numerical methods to real problems, you will find this book full of interest … the book acts as a useful introduction to several important, more general, issues in scientific computing.' The Mathematical GazetteTable of Contents1. Getting started; 2. Initial value problems; 3. Boundary value problems; 4. Delay differential equations.

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Book SynopsisThe first MATLAB-based numerical methods textbook specifically for bioengineers, including topics on hypothesis testing, plus numerous examples drawn exclusively from biomedical engineering applications. This is an ideal core text for one-semester undergraduate courses, and is also a valuable reference for anyone interested in the quantitative aspects of biology research.Trade Review'I think this book is a winner … [it] is really easy to read and places frameworks for numerical analysis into realistic bioengineering concepts that students will find familiar and relevant. This is most evident in the excellent boxed examples, but also in many of the homework problems. I also really liked the 'key points to consider' at the end of the chapters - these are useful reminders for the students. Finally, the book presents bioinformatics in a manageable fashion that should help demystify this subject for interested students.' K. Jane Grande-Allen, Rice UniversityTable of Contents1. Types and sources of numerical error; 2. Systems of linear equations; 3. Statistics and probability; 4. Hypothesis testing; 5. Root finding techniques for nonlinear equations; 6. Numerical quadrature; 7. Numerical integration of ordinary differential equations; 8. Nonlinear data regression and optimization; 9. Basic algorithms of bioinformatics; Appendix A. Introduction to MATLAB; Appendix B. Location of nodes for Gauss-Legendre quadrature.

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Book SynopsisNow in its third edition, this outstanding textbook explains everything you need to get started using MATLAB . It contains concise explanations of essential MATLAB commands, as well as easily understood instructions for using MATLAB's programming features, graphical capabilities, simulation models, and rich desktop interface. MATLAB 8 and its new user interface is treated extensively in the book. New features in this edition include: a complete treatment of MATLAB's publish feature; new material on MATLAB graphics, enabling the user to master quickly the various symbolic and numerical plotting routines; and a robust presentation of MuPAD and how to use it as a stand-alone platform. The authors have also updated the text throughout, reworking examples and exploring new applications. The book is essential reading for beginners, occasional users and experienced users wishing to brush up their skills. Further resources are available from the authors' website at www-math.umd.edu/schol/a-guTrade ReviewReview of previous edition: 'Major highlights of the book are completely transparent examples of classical yet always intriguing mathematical, statistical, engineering, economics, and physics problems. In addition, the book explains a seamless use with Microsoft Word for integrating MATLAB® outputs with documents, reports, presentations, or other online processes. Advanced topics with examples include: Monte Carlo simulation, population dynamics, and linear programming. … an outstanding textbook, and, likewise, should be an integral part of the technical reference shelf for most IT professionals. It is a great resource for wherever MATLAB® is available!' ACM UbiquityReview of previous edition: 'This is a short, focused introduction to MATLAB®, a comprehensive software system for mathematical and technical computing. For the beginner it explains everything needed to start using MATLAB®, while experienced users ... will find much useful information here.' L'enseignement mathematiqueTable of ContentsPreface; 1. Getting started; 2. MATLAB basics; 3. Interacting with MATLAB; Practice Set A. Algebra and arithmetic; 4. Beyond the basics; 5. MATLAB graphics; 6. MATLAB programming; 7. Publishing and M-books; Practice Set B. Math, graphics, and programming; 8. MuPAD; 9. Simulink; 10. GUIs; 11. Applications; Practice Set C. Developing your MATLAB skills; 12. Troubleshooting; Solutions to the practice sets; Glossary; Index.

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Book SynopsisTrade Review"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. "Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. "What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)Table of ContentsPart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The "Right Model" for the "Right Purpose": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models" 21. Ethics and Data Analytics 22. IBM Watson

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Book SynopsisA self-contained introduction to abstract interpretation-based static analysis, an essential resource for students, developers, and users.Static program analysis, or static analysis, aims to discover semantic properties of programs without running them. It plays an important role in all phases of development, including verification of specifications and programs, the synthesis of optimized code, and the refactoring and maintenance of software applications. This book offers a self-contained introduction to static analysis, covering the basics of both theoretical foundations and practical considerations in the use of static analysis tools. By offering a quick and comprehensive introduction for nonspecialists, the book fills a notable gap in the literature, which until now has consisted largely of scientific articles on advanced topics.The text covers the mathematical foundations of static analysis, including semantics, semantic abstraction, and computation of program inv

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Book SynopsisThis textbook provides a self-contained introduction to linear programming using MATLAB software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, nonlinear functions, and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors. Only a basic knowledge of linear algebra and calculus is required to understand this textbook, which is geared toward junior and senior-level undergraduate stud

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Book SynopsisWritten at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space.Table of ContentsPREFACE xv ACKNOWLEDGMENTS xvii PART I BASIC CORRELATION STRUCTURES 1 RBasics 3 1.1 Getting Started, 3 1.2 Special R Conventions, 5 1.3 Common Structures, 5 1.4 Common Functions, 6 1.5 Time Series Functions, 6 1.6 Importing Data, 7 Exercises, 7 2 Review of Regression and More About R 8 2.1 Goals of this Chapter, 8 2.2 The Simple(ST) Regression Model, 8 2.2.1 Ordinary Least Squares, 8 2.2.2 Properties of OLS Estimates, 9 2.2.3 Matrix Representation of the Problem, 9 2.3 Simulating the Data from a Model and Estimating the Model Parameters in R, 9 2.3.1 Simulating Data, 9 2.3.2 Estimating the Model Parameters in R, 9 2.4 Basic Inference for the Model, 12 2.5 Residuals Analysis—What Can Go Wrong…, 13 2.6 Matrix Manipulation in R, 15 2.6.1 Introduction, 15 2.6.2 OLS the Hard Way, 15 2.6.3 Some Other Matrix Commands, 16 Exercises, 16 3 The Modeling Approach Taken in this Book and Some Examples of Typical Serially Correlated Data 18 3.1 Signal and Noise, 18 3.2 Time Series Data, 19 3.3 Simple Regression in the Framework, 20 3.4 Real Data and Simulated Data, 20 3.5 The Diversity of Time Series Data, 21 3.6 Getting Data Into R, 24 3.6.1 Overview, 24 3.6.2 The Diskette and the scan() and ts() Functions—New York City Temperatures, 25 3.6.3 The Diskette and the read.table() Function—The Semmelweis Data, 25 3.6.4 Cut and Paste Data to a Text Editor, 26 Exercises, 26 4 Some Comments on Assumptions 28 4.1 Introduction, 28 4.2 The Normality Assumption, 29 4.2.1 Right Skew, 30 4.2.2 Left Skew, 30 4.2.3 Heavy Tails, 30 4.3 Equal Variance, 31 4.3.1 Two-Sample t-Test, 31 4.3.2 Regression, 31 4.4 Independence, 31 4.5 Power of Logarithmic Transformations Illustrated, 32 4.6 Summary, 34 Exercises, 34 5 The Autocorrelation Function And AR(1), AR(2) Models 35 5.1 Standard Models—What are the Alternatives to White Noise?, 35 5.2 Autocovariance and Autocorrelation, 36 5.2.1 Stationarity, 36 5.2.2 A Note About Conditions, 36 5.2.3 Properties of Autocovariance, 36 5.2.4 White Noise, 37 5.2.5 Estimation of the Autocovariance and Autocorrelation, 37 5.3 The acf() Function in R, 37 5.3.1 Background, 37 5.3.2 The Basic Code for Estimating the Autocovariance, 38 5.4 The First Alternative to White Noise: Autoregressive Errors—AR(1), AR(2), 40 5.4.1 Definition of the AR(1) and AR(2) Models, 40 5.4.2 Some Preliminary Facts, 40 5.4.3 The AR(1) Model Autocorrelation and Autocovariance, 41 5.4.4 Using Correlation and Scatterplots to Illustrate the AR(1) Model, 41 5.4.5 The AR(2) Model Autocorrelation and Autocovariance, 41 5.4.6 Simulating Data for AR(m) Models, 42 5.4.7 Examples of Stable and Unstable AR(1) Models, 44 5.4.8 Examples of Stable and Unstable AR(2) Models, 46 Exercises, 49 6 The Moving Average Models MA(1) And MA(2) 51 6.1 The Moving Average Model, 51 6.2 The Autocorrelation for MA(1) Models, 51 6.3 A Duality Between MA(l) And AR(m) Models, 52 6.4 The Autocorrelation for MA(2) Models, 52 6.5 Simulated Examples of the MA(1) Model, 52 6.6 Simulated Examples of the MA(2) Model, 54 6.7 AR(m) and MA(l) model acf() Plots, 54 Exercises, 57 PART II ANALYSIS OF PERIODIC DATA AND MODEL SELECTION 7 Review of Transcendental Functions and Complex Numbers 61 7.1 Background, 61 7.2 Complex Arithmetic, 62 7.2.1 The Number i, 62 7.2.2 Complex Conjugates, 62 7.2.3 The Magnitude of a Complex Number, 62 7.3 Some Important Series, 63 7.3.1 The Geometric and Some Transcendental Series, 63 7.3.2 A Rationale for Euler’s Formula, 63 7.4 Useful Facts About Periodic Transcendental Functions, 64 Exercises, 64 8 The Power Spectrum and the Periodogram 65 8.1 Introduction, 65 8.2 A Definition and a Simplified Form for p(f ), 66 8.3 Inverting p(f ) to Recover the Ck Values, 66 8.4 The Power Spectrum for Some Familiar Models, 68 8.4.1 White Noise, 68 8.4.2 The Spectrum for AR(1) Models, 68 8.4.3 The Spectrum for AR(2) Models, 70 8.5 The Periodogram, a Closer Look, 72 8.5.1 Why is the Periodogram Useful?, 72 8.5.2 Some Na¨ýve Code for a Periodogram, 72 8.5.3 An Example—The Sunspot Data, 74 8.6 The Function spec.pgram() in R, 75 Exercises, 77 9 Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram 79 9.1 Why is Smoothing Required?, 79 9.2 Smoothing, Bias, and Variance, 79 9.3 Smoothers Used in R, 80 9.3.1 The R Function lowess(), 81 9.3.2 The R Function smooth.spline(), 82 9.3.3 Kernel Smoothers in spec.pgram(), 83 9.4 Smoothing the Periodogram for a Series With a Known and Unknown Period, 85 9.4.1 Period Known, 85 9.4.2 Period Unknown, 86 9.5 Summary, 87 Exercises, 87 10 A Regression Model for Periodic Data 89 10.1 The Model, 89 10.2 An Example: The NYC Temperature Data, 91 10.2.1 Fitting a Periodic Function, 91 10.2.2 An Outlier, 92 10.2.3 Refitting the Model with the Outlier Corrected, 92 10.3 Complications 1: CO2 Data, 93 10.4 Complications 2: Sunspot Numbers, 94 10.5 Complications 3: Accidental Deaths, 96 10.6 Summary, 96 Exercises, 96 11 Model Selection and Cross-Validation 98 11.1 Background, 98 11.2 Hypothesis Tests in Simple Regression, 99 11.3 A More General Setting for Likelihood Ratio Tests, 101 11.4 A Subtlety Different Situation, 104 11.5 Information Criteria, 106 11.6 Cross-validation (Data Splitting): NYC Temperatures, 108 11.6.1 Explained Variation, R2, 108 11.6.2 Data Splitting, 108 11.6.3 Leave-One-Out Cross-Validation, 110 11.6.4 AIC as Leave-One-Out Cross-Validation, 112 11.7 Summary, 112 Exercises, 113 12 Fitting Fourier series 115 12.1 Introduction: More Complex Periodic Models, 115 12.2 More Complex Periodic Behavior: Accidental Deaths, 116 12.2.1 Fourier Series Structure, 116 12.2.2 R Code for Fitting Large Fourier Series, 116 12.2.3 Model Selection with AIC, 117 12.2.4 Model Selection with Likelihood Ratio Tests, 118 12.2.5 Data Splitting, 119 12.2.6 Accidental Deaths—Some Comment on Periodic Data, 120 12.3 The Boise River Flow data, 121 12.3.1 The Data, 121 12.3.2 Model Selection with AIC, 122 12.3.3 Data Splitting, 123 12.3.4 The Residuals, 123 12.4 Where Do We Go from Here?, 124 Exercises, 124 13 Adjusting for AR(1) Correlation in Complex Models 125 13.1 Introduction, 125 13.2 The Two-Sample t-Test—UNCUT and Patch-Cut Forest, 125 13.2.1 The Sleuth Data and the Question of Interest, 125 13.2.2 A Simple Adjustment for t-Tests When the Residuals Are AR(1), 128 13.2.3 A Simulation Example, 129 13.2.4 Analysis of the Sleuth Data, 131 13.3 The Second Sleuth Case—Global Warming, A Simple Regression, 132 13.3.1 The Data and the Question, 132 13.3.2 Filtering to Produce (Quasi-)Independent Observations, 133 13.3.3 Simulated Example—Regression, 134 13.3.4 Analysis of the Regression Case, 135 13.3.5 The Filtering Approach for the Logging Case, 136 13.3.6 A Few Comments on Filtering, 137 13.4 The Semmelweis Intervention, 138 13.4.1 The Data, 138 13.4.2 Why Serial Correlation?, 139 13.4.3 How This Data Differs from the Patch/Uncut Case, 139 13.4.4 Filtered Analysis, 140 13.4.5 Transformations and Inference, 142 13.5 The NYC Temperatures (Adjusted), 142 13.5.1 The Data and Prediction Intervals, 142 13.5.2 The AR(1) Prediction Model, 144 13.5.3 A Simulation to Evaluate These Formulas, 144 13.5.4 Application to NYC Data, 146 13.6 The Boise River Flow Data: Model Selection With Filtering, 147 13.6.1 The Revised Model Selection Problem, 147 13.6.2 Comments on R2 and R2 pred, 147 13.6.3 Model Selection After Filtering with a Matrix, 148 13.7 Implications of AR(1) Adjustments and the “Skip” Method, 151 13.7.1 Adjustments for AR(1) Autocorrelation, 151 13.7.2 Impact of Serial Correlation on p-Values, 152 13.7.3 The “skip” Method, 152 13.8 Summary, 152 Exercises, 153 PART III COMPLEX TEMPORAL STRUCTURES 14 The Backshift Operator, the Impulse Response Function, and General ARMA Models 159 14.1 The General ARMA Model, 159 14.1.1 The Mathematical Formulation, 159 14.1.2 The arima.sim() Function in R Revisited, 159 14.1.3 Examples of ARMA(m,l) Models, 160 14.2 The Backshift (Shift, Lag) Operator, 161 14.2.1 Definition of B, 161 14.2.2 The Stationary Conditions for a General AR(m) Model, 161 14.2.3 ARMA(m,l) Models and the Backshift Operator, 162 14.2.4 More Examples of ARMA(m,l) Models, 162 14.3 The Impulse Response Operator—Intuition, 164 14.4 Impulse Response Operator, g(B)—Computation, 165 14.4.1 Definition of g(B), 165 14.4.2 Computing the Coefficients, gj., 165 14.4.3 Plotting an Impulse Response Function, 166 14.5 Interpretation and Utility of the Impulse Response Function, 167 Exercises, 167 15 The Yule–Walker Equations and the Partial Autocorrelation Function 169 15.1 Background, 169 15.2 Autocovariance of an ARMA(m,l) Model, 169 15.2.1 A Preliminary Result, 169 15.2.2 The Autocovariance Function for ARMA(m,l) Models, 170 15.3 AR(m) and the Yule–Walker Equations, 170 15.3.1 The Equations, 170 15.3.2 The R Function ar.yw() with an AR(3) Example, 171 15.3.3 Information Criteria-Based Model Selection Using ar.yw(), 173 15.4 The Partial Autocorrelation Plot, 174 15.4.1 A Sequence of Hypothesis Tests, 174 15.4.2 The pacf() Function—Hypothesis Tests Presented in a Plot, 174 15.5 The Spectrum For Arma Processes, 175 15.6 Summary, 177 Exercises, 178 16 Modeling Philosophy and Complete Examples 180 16.1 Modeling Overview, 180 16.1.1 The Algorithm, 180 16.1.2 The Underlying Assumption, 180 16.1.3 An Example Using an AR(m) Filter to Model MA(3), 181 16.1.4 Generalizing the “Skip” Method, 184 16.2 A Complex Periodic Model—Monthly River Flows, Furnas 1931–1978, 185 16.2.1 The Data, 185 16.2.2 A Saturated Model, 186 16.2.3 Building an AR(m) Filtering Matrix, 187 16.2.4 Model Selection, 189 16.2.5 Predictions and Prediction Intervals for an AR(3) Model, 190 16.2.6 Data Splitting, 191 16.2.7 Model Selection Based on a Validation Set, 192 16.3 A Modeling Example—Trend and Periodicity: CO2 Levels at Mauna Lau, 193 16.3.1 The Saturated Model and Filter, 193 16.3.2 Model Selection, 194 16.3.3 How Well Does the Model Fit the Data?, 197 16.4 Modeling Periodicity with a Possible Intervention—Two Examples, 198 16.4.1 The General Structure, 198 16.4.2 Directory Assistance, 199 16.4.3 Ozone Levels in Los Angeles, 202 16.5 Periodic Models: Monthly, Weekly, and Daily Averages, 205 16.6 Summary, 207 Exercises, 207 PART IV SOME DETAILED AND COMPLETE EXAMPLES 17 Wolf’s Sunspot Number Data 213 17.1 Background, 213 17.2 Unknown Period ⇒ Nonlinear Model, 214 17.3 The Function nls() in R, 214 17.4 Determining the Period, 216 17.5 Instability in the Mean, Amplitude, and Period, 217 17.6 Data Splitting for Prediction, 220 17.6.1 The Approach, 220 17.6.2 Step 1—Fitting One Step Ahead, 222 17.6.3 The AR Correction, 222 17.6.4 Putting it All Together, 223 17.6.5 Model Selection, 223 17.6.6 Predictions Two Steps Ahead, 224 17.7 Summary, 226 Exercises, 226 18 An Analysis of Some Prostate and Breast Cancer Data 228 18.1 Background, 228 18.2 The First Data Set, 229 18.3 The Second Data Set, 232 18.3.1 Background and Questions, 232 18.3.2 Outline of the Statistical Analysis, 233 18.3.3 Looking at the Data, 233 18.3.4 Examining the Residuals for AR(m) Structure, 235 18.3.5 Regression Analysis with Filtered Data, 238 Exercises, 243 19 Christopher Tennant/Ben Crosby Watershed Data 245 19.1 Background and Question, 245 19.2 Looking at the Data and Fitting Fourier Series, 246 19.2.1 The Structure of the Data, 246 19.2.2 Fourier Series Fits to the Data, 246 19.2.3 Connecting Patterns in Data to Physical Processes, 246 19.3 Averaging Data, 248 19.4 Results, 250 Exercises, 250 20 Vostok Ice Core Data 251 20.1 Source of the Data, 251 20.2 Background, 252 20.3 Alignment, 253 20.3.1 Need for Alignment, and Possible Issues Resulting from Alignment, 253 20.3.2 Is the Pattern in the Temperature Data Maintained?, 254 20.3.3 Are the Dates Closely Matched?, 254 20.3.4 Are the Times Equally Spaced?, 255 20.4 A Na¨ýve Analysis, 256 20.4.1 A Saturated Model, 256 20.4.2 Model Selection, 258 20.4.3 The Association Between CO2 and Temperature Change, 258 20.5 A Related Simulation, 259 20.5.1 The Model and the Question of Interest, 259 20.5.2 Simulation Code in R, 260 20.5.3 A Model Using all of the Simulated Data, 261 20.5.4 A Model Using a Sample of 283 from the Simulated Data, 262 20.6 An AR(1) Model for Irregular Spacing, 265 20.6.1 Motivation, 265 20.6.2 Method, 266 20.6.3 Results, 266 20.6.4 Sensitivity Analysis, 267 20.6.5 A Final Analysis, Well Not Quite, 268 20.7 Summary, 269 Exercises, 270 Appendix A Using Datamarket 273 A.1 Overview, 273 A.2 Loading a Time Series in Datamarket, 277 A.3 Respecting Datamarket Licensing Agreements, 280 Appendix B AIC is PRESS! 281 B.1 Introduction, 281 B.2 PRESS, 281 B.3 Connection to Akaike’s Result, 282 B.4 Normalization and R2, 282 B.5 An example, 283 B.6 Conclusion and Further Comments, 283 Appendix C A 15-Minute Tutorial on Nonlinear Optimization 284 C.1 Introduction, 284 C.2 Newton’s Method for One-Dimensional Nonlinear Optimization, 284 C.3 A Sequence of Directions, Step Sizes, and a Stopping Rule, 285 C.4 What Could Go Wrong?, 285 C.5 Generalizing the Optimization Problem, 286 C.6 What Could Go Wrong—Revisited, 286 C.7 What Can be Done?, 287 REFERENCES 291 INDEX 293

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