R in a Nutshell: A Desktop Quick Reference Review

Average Reviews:

(More customer reviews)'R in a Nutshell' is the essential introductory book on R. Do not try to learn R without it.
I made two attempts to learn R before purchasing this book. In both previous attempts, I had to abort and use another tool to solve my problem because it was taking me too long to accomplish very simple things in R.
The reason R is hard to learn is that its documentation is organized for statisticians that already know R, but have forgotten a detail or two. There are a few other books on learning R, but they are setup like a college course - complete the entire book and THEN you can actually accomplish something.
R in a Nutshell allows you to get working immediately. Simply lookup what you need to do. The firsts thing I did was load a file and make a histogram. I found that stuff in the section on "Loading Data" and the section on charts. In no time I was making stacked area charts for cohorts. Now R is an essential tool for me - and I haven't even taken the time to learn it well! With this book, I don't have to. I can learn as I go. So I actually use R.
Do not R without it.

Click Here to see more reviews about: R in a Nutshell: A Desktop Quick Reference


What people are saying about R in a Nutshell

"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University

"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks." --David M. Smith, Editor of the "Revolutions" blog at REvolution Computing

Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.

Understand the basics of the language, including the nature of R objects
Learn how to write R functions and build your own packages
Work with data through visualization, statistical analysis, and other methods
Explore the wealth of packages contributed by the R community
Become familiar with the lattice graphics package for high-level data visualization
Learn about bioinformatics packages provided by Bioconductor

Get 20% OFF

Click here for more information about R in a Nutshell: A Desktop Quick Reference

Read More...

R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R) Review

Average Reviews:

(More customer reviews)Skip this paragraph if you know about R and Excel.... R is a powerful programming language that is useful for generating scientific graphics (both simple and extremely complex) and for statistical work. Unfortunately it has a steep learning curve and many say the help files are not particularly helpful for novices. Excel has a user friendly system for entering data and doing basic graphics but has relatively very limited tools for statistics or complex scientific graphics. Combining the strengths of the two is the goal of this book and the free software that goes with it.
With the tools described in this book the user can point and click their way to common analyses and graphics inside of Excel without having to learn to write code in the R programming language. Both the software and book are good but not great because they do not add much to the existing tools for R.
Years ago, John Fox wrote a point-and-click code-generating add-on package for R called R Commander that revolutionized the usability of R. Inside of the R programming environment you can download the Rcmdr package and type library(Rcmdr) and get practically all the same functionality as the tools provided here. What the authors of this book do is bring the functionality of Rcmdr into a Excel as an add-on to the Excel 2003 menus or 2007 ribbon. The implementation is surprisingly smooth (including adding nice right-click menu items) and bug free.
The book itself is mostly nicely rendered color pictures. Think of it as a very well annotated PowerPoint presentation. You will be able to quickly page through it and it is well indexed. The less than 10 page appendix which explains how to install the R packages and required services (or how to install from scratch) is probably the most useful part of the book. The authors do not focus much on the "behind the scenes" strengths of their work which allow you to recalculate and pass information into and out of R "on the fly." However, they do include a few worksheets that demonstrate the ability to pass information into R and return graphics effortlessly. Think of this as the ability to add sliders and push buttons to Excel and have instantly updated high quality graphics. Unfortunately, they only give one example where they use there new RApply function to return a calculated value from R into Excel. There clearly is a lot of functionality but the book does not explain it.
If you are just starting with R this book will probably be a HUGE help to you because it saves you from memorizing a lot of code and it will help you learn how to write code by showing you the commands that were used to generate analyses and graphics. However, if you have experience with R and Rcmdr you probably want to save your money.

Click Here to see more reviews about: R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R)

In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.

Get 21% OFF

Click here for more information about R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R)

Read More...

Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Review

Average Reviews:

(More customer reviews)A survey of remarkable breadth, listing hundreds of sources: 50-100 items per chapter, majority of them from conference proceedings. Evidently, this kind of volume does not let the author discuss any particular paper or topic in detail; at best, a reference is covered with a short paragraph. This does create a problem: the book's value is exhausted once the reader looks up the topic of interest, and moves on to the suggested references. "Temporal data mining" could do with a little more editing, but I am quite impressed with it the way it is, an authoritative and wide-ranging introduction to an interesting topic.

Click Here to see more reviews about: Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining.Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/

Get 29% OFF

Click here for more information about Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Read More...

R Cookbook (O'Reilly Cookbooks) Review

Average Reviews:

(More customer reviews)I'd give this book ten stars if I could. I bought one copy for the office and one for my house. This guy has the ability to write simply and with the mind set of people who are busy and want to get results right away. Of course we'd all love to be leisurely scholars and plow through theory and practice but most of us just need to get things done. A good example is the way he treats ARIMA. He warns you about using auto.arima but does not hide it from you because it is "dangerous." The book is full of tips, well organized and is oriented towards beginners, though it gets into depth. So many of the R books I've read absolutely pound you with up front details, some of which relate to obscure concerns, rather than starting with a task. For example, on page 199 he writes "Problem -- you want to count the relative frequency of certain observations in your sample" Next is "Solution" -- and he explains just the minimum needed to do that job. Some of the tips are just simple time savers, such as the function head(dataframe) to show a few of the dataframe rows at the start and tail(dataframe) to show a few at the end. Finally .... I don't know this writer personally, but I hope he keeps on writing because it is a craft he has thoroughly absorbed somewhere along the line.Bill Yarberry, Houston, TX


Click Here to see more reviews about: R Cookbook (O'Reilly Cookbooks)


With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.

Create vectors, handle variables, and perform other basic functions
Input and output data
Tackle data structures such as matrices, lists, factors, and data frames
Work with probability, probability distributions, and random variables
Calculate statistics and confidence intervals, and perform statistical tests
Create a variety of graphic displays
Build statistical models with linear regressions and analysis of variance (ANOVA)
Explore advanced statistical techniques, such as finding clusters in your data

"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time." —Jeffrey Ryan, software consultant and R package author

Get 22% OFF

Click here for more information about R Cookbook (O'Reilly Cookbooks)

Read More...

Econometric Analysis of Count Data Review

Average Reviews:

(More customer reviews)excellent. the book provides good examples and helps the readers to relate the huge literature and how one paper is related to another one.

Click Here to see more reviews about: Econometric Analysis of Count Data

The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed. Finally, applications are reviewed in various fields.

Get 21% OFF

Click here for more information about Econometric Analysis of Count Data

Read More...

Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) Review

Average Reviews:

(More customer reviews)Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).


Click Here to see more reviews about: Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics)

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

Get 20% OFF

Click here for more information about Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics)

Read More...

New Introduction to Multiple Time Series Analysis Review

Average Reviews:

(More customer reviews)If you are looking for a book on VARs and cointegration, this is it.
Very clearly written, and with numerical applications of every new concept (so that you can check the accuracy of your codes ...)
Its a significantly improved version of the last edition.
Highly recommended.


Click Here to see more reviews about: New Introduction to Multiple Time Series Analysis

This is the new and totally revised edition of Lütkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

Get 21% OFF

Click here for more information about New Introduction to Multiple Time Series Analysis

Read More...

Mixed Effects Models and Extensions in Ecology with R Review

Average Reviews:

(More customer reviews)Many applications in ecology clearly are not amenable to use of the general linear model due to violations of its assumptions. In fact, in most projects I work on, things like correlation among the errors, nonconstant error variance, etc., are the rule, rather than the exception. If you are looking for an applied text dealing with these types of situations with lots of examples, and demonstrations on analysis in R, then you should get this book. It does not delve into theory; there are plenty of other textbooks where you can fill in those details if you are interested. Rather, this book would be ideally suited for quantitative ecologists, biometricians, and statistical consultants who work in life sciences. Another nice thing is that the book does not assume you are an "R expert". Well done.

Click Here to see more reviews about: Mixed Effects Models and Extensions in Ecology with R

This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

Get 20% OFF

Click here for more information about Mixed Effects Models and Extensions in Ecology with R

Read More...

Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics) Review

Average Reviews:

(More customer reviews)Very good introductory text for nonparametric regression. This book first gives motivation for the processes, outlines the methods of smoothing, and then covers various regression techniques. Not particularly mathematical, although some linear algebra knowledge is necessary. Very accessible as a first course. Does not go very in-depth into the theory of any of the methods, however.

Click Here to see more reviews about: Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics)

Incorporates mixed-effects modeling techniques for more powerful and efficient methodsThis book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques.The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis.Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices.With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Get 17% OFF

Click here for more information about Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics)

Read More...

Modeling Financial Time Series with S-PLUS® Review

Average Reviews:

(More customer reviews)This is an excellent book on financial econometrics, very practical yet rigorous. I wish all econometrics/statistics textbook could like this. Basic theory followed by practical examples - real life examples, not simplified ones like in other books. The authors gave detailed instructions on how to implement various econometric models, i.e. multi-factor models, GARCH, MGARCH, long memory models, state-space, etc. Most econometrics textbooks are at two extremes, they are either too theoretical (you still don't know how to put those models in real life), or too simple (lack of mathematical rigor and without advanced applications). This book is a combination of both worlds, computer codes/math models, and real life examples (some really good ones). A lot of cutting-edge techniques and advanced topics are also covered.

Click Here to see more reviews about: Modeling Financial Time Series with S-PLUS®

This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance.Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.

Get 46% OFF

Click here for more information about Modeling Financial Time Series with S-PLUS®

Read More...

Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

Average Reviews:

(More customer reviews)Issues of missing data in longitudinal studies are very important in the design and analysis of clinical trials. This is such an important statistical topic, that many excellent books have been written about it. One of the earliest and a landmark text was the book by Rubin and Little which was recently updated in the second edition. Mixed linear models for longitudinal data provide an effective method for dealing with several types of missingness as does multiple imputation. Pattern mixture models are also very useful. Molenberghs and Kennard, Verbeke and Molenberghs and Rubin all cover these topic well in their excellent texts.
What then is the advantage of this text by Daniels and Hogan?
1. It is slightly more current than the others
2. It combines theory and application very nicely
3. A series of seven real data sets from real clinical trials and epidemiologic studies are presented up front in Chapter 1 and used throughout to illustrate practical advantages and disadvantages of the various techniques covered in the latter chapters
4. It covers Bayesian modeling and sensitivity analysis in more depth that most of its competitors
Only Molenberghs and Kennard match it in the depth of coverage on theory and applications. But they do not provide the coverage of Bayesian methods the way Daniels and Hogan do.
For these reasons I recommend this book to the practicing biostatisticians working on clinical trials even if the texts listed below are alresdy on their bookshelves.
I) Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. (2002). "Analysis of Longitudinal Data" 2nd Edition. Oxfrod University Press, Oxford.
II) Fitzmaurice, G. M., Laird, N.M. and Ware, J. H. (2004). "Applied ongitudinal Analysis". John Wiley & Sons, New York.
III) Little, R. J. A. and Rubin, D. B. (2002) "Statistical Analysis with Missing Data" 2nd Edition, John Wiley & Sons, New York
IV) Molenberghs, G and Kennard, M. G. (2007). "Missing Data in Clinical Studies" John Wiley & Sons, Chichester.
V) Molenberghs, G. and Verbeke, G. (2005). "Models for Discrete Longitudinal Data". Springer-Verlag, New York.
VI) Pinheiro, J. C. and Bates, D. M. (2000). "Mixed Effects Models in S and S-Plus". Springer-Verlag, New York.
VII) Rubin, D. B. (1987). "Multiple Imputation for Nonresponse in Surveys" John Wiley & Sons, New York.
VIII) Tsiatis, A. A. (2006). "Semiparametric Theory and Missing Data" Dpringer-Verlag, New York.
IX) Verbeke, G and Molenberghs, G. (1997). "Linear Mixed Models in Practice: A SAS-Oriented Approach" Springer-Verlag, New York.
X) Verbeke, G and Molenberghs, G. (2000). "Linear Mixed Models for Longitudinal Data" Springer-Verlag, New York.


Click Here to see more reviews about: Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues.
The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness.
With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

Get 14% OFF

Click here for more information about Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Read More...

Introduction to Time Series Modeling (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

Average Reviews:

(More customer reviews)Here is the table of contents from the CRC site.
Introduction and Preparatory Analysis
Time Series Data
Classification of Time Series
Objectives of Time Series Analysis
Preprocessing of Time Series
Organization of This Book
The Covariance Function
The Distribution of Time Series and Stationarity
The Autocovariance Function of Stationary Time Series
Estimation of the Autocovariance Function
Multivariate Time Series and Scatterplots
Cross-Covariance Function and Cross-Correlation Function
The Power Spectrum and the Periodogram
The Power Spectrum
The Periodogram
Averaging and Smoothing of the Periodogram
Computational Method of Periodogram
Computation of the Periodogram by Fast Fourier Transform
Statistical Modeling
Probability Distributions and Statistical Models
K-L Information and the Entropy Maximization Principle
Estimation of the K-L Information and Log-Likelihood
Estimation of Parameters by the Maximum Likelihood Method
Akaike Information Criterion (AIC)
Transformation of Data
The Least Squares Method
Regression Models and the Least Squares Method
Householder Transformation Method
Selection of Order by AIC
Addition of Data and Successive Householder Reduction
Variable Selection by AIC
Analysis of Time Series Using ARMA Models
ARMA Model
The Impulse Response Function
The Autocovariance Function
The Relation between AR Coefficients and the PARCOR
The Power Spectrum of the ARMA Process
The Characteristic Equation
The Multivariate AR Model
Estimation of an AR Model
Fitting an AR Model
Yule-Walker Method and Levinson's Algorithm
Estimation of an AR Model by the Least Squares Method
Estimation of an AR Model by the PARCOR Method
Large Sample Distribution of the Estimates
Yule-Walker Method for MAR Model
Least Squares Method for MAR Model
The Locally Stationary AR Model
Locally Stationary AR Model
Automatic Partitioning of the Time Interval
Precise Estimation of a Change Point
Analysis of Time Series with a State-Space Model
The State-Space Model
State Estimation via the Kalman Filter
Smoothing Algorithms
Increasing Horizon Prediction of the State
Prediction of Time Series
Likelihood Computation and Parameter Estimation for a Time Series Model
Interpolation of Missing Observations
Estimation of the ARMA Model
State-Space Representation of the ARMA Model
Initial State of an ARMA Model
Maximum Likelihood Estimate of an ARMA Model
Initial Estimates of Parameters
Estimation of Trends
The Polynomial Trend Model
Trend Component Model--Model for Probabilistic Structural Changes
Trend Model
The Seasonal Adjustment Model
Seasonal Component Model
Standard Seasonal Adjustment Model
Decomposition Including an AR Component
Decomposition Including a Trading-Day Effect
Time-Varying Coefficient AR Model
Time-Varying Variance Model
Time-Varying Coefficient AR Model
Estimation of the Time-Varying Spectrum
The Assumption on System Noise for the Time-Varying Coefficient AR Model
Abrupt Changes of Coefficients
Non-Gaussian State-Space Model
Necessity of Non-Gaussian Models
Non-Gaussian State-Space Models and State Estimation
Numerical Computation of the State Estimation Formula
Non-Gaussian Trend Model
A Time-Varying Variance Model
Applications of Non-Gaussian State-Space Model
The Sequential Monte Carlo Filter
The Nonlinear Non-Gaussian State-Space Model and Approximations of Distributions
Monte Carlo Filter
Monte Carlo Smoothing Method
Nonlinear Smoothing
Simulation
Generation of Uniform Random Numbers
Generation of Gaussian White Noise
Simulation Using a State-Space Model
Simulation with Non-Gaussian Model
Appendix A: Algorithms for Nonlinear Optimization
Appendix B: Derivation of Levinson's Algorithm
Appendix C: Derivation of the Kalman Filter and Smoother Algorithms
Appendix D: Algorithm for the Monte Carlo Filter
Bibliography

Click Here to see more reviews about: Introduction to Time Series Modeling (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the various tools for handling them.The book employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods, including the Kalman filter, the non-Gaussian filter, and the sequential Monte Carlo filter, for the state-space models. Taking a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike, the author derives various methods of parameter estimation, such as the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by the Akaike information criterion (AIC). Along with simulation methods, he also covers standard stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model.With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It encourages readers to build models for their own real-life problems.

Get 14% OFF

Click here for more information about Introduction to Time Series Modeling (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Read More...

Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities Review

Average Reviews:

(More customer reviews)Royle & Dorazio (2008): A truly synthetic overview
This book not only illustrates, and presents R and WinBUGS code for, plenty of methods for inference about distribution and abundance in animal and plant populations and communities; it does much more. It presents a truly synthetic overview of these methods and makes the reader understand how they relate to each other. At the same time, the authors succeed extremely well in teaching a modern, "organic way" of statistical modeling -- where one first thinks hard about how the observed data might have arisen via a combination of stochastic processes (the book is about hierarchical models, remember) and then builds a custom statistical model for exactly those processes. This combination of presenting a unifying synthesis of a vast array of methods and showing how to model a study system organically in my view is unique among the currently available statistical ecology books.

Click Here to see more reviews about: Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)* Development of classical, likelihood-based procedures for inference, as well asBayesian methods of analysis* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS* Computing support in technical appendices in an online companion web site

Get 18% OFF

Click here for more information about Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities

Read More...

Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics) Review

Average Reviews:

(More customer reviews)Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.
The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.
Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.
A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).


Click Here to see more reviews about: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics)

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Get 20% OFF

Click here for more information about Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics)

Read More...

Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Review

Average Reviews:

(More customer reviews)This book is, bar none, the best book on longitudinal analysis in social sciences.
The book has three outstanding features that make it the must-have for researchers who conduct longitudinal studies. First, the book has numerous examples that use data from real studies, collected by prominent scholars in this area. With the help of the accompanying website at UCLA, you will learn how to set up data files, which is crucial in longitudinal analysis. The sample codes and data files in SAS, SPSS, Stata, MLwiN, Mplus, HLM, and Splus will allow you to replicate the analyses. The authors use every effort to explain the results in plain, understandable language. They use a lot of graphs and tables to compare different nested models and help you to choose the one that best describes your data. It feels like you have an excellent tutor by your side when you are reading this book.
Second, the coverage of this book is comprehensive. Part I covers the regular growth curve modeling and multilevel modeling, with a few chapters dealing with time-varying covariates, discontinuous and nonlinear change. Part II covers discrete-time and continuous-time survival analysis. If you are conducting a longitudinal study, chances are you will find a technique in this book that suits you just right.
Third, the book is quite deep. Although it gears toward applications of different longitudinal analyses, it is no cakewalk. You need at least some background in multiple regression and multivariate statistics. I think the treatment of mathematics (both concepts and formulas) is just right. In some sections you may need to revisit them often in order to fully understand the subject.


Click Here to see more reviews about: Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Change is constant in everyday life.Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful.Beyond these natural processes and events, external forces and interventions instigate and disrupt change:test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences.It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: Downloadable data sets Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more Additional material for data analysis

Get 23% OFF

Click here for more information about Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Read More...