An Introduction to Programming with IDL: Interactive Data Language Review

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(More customer reviews)Nothing is more intimidating to a new user of IDL than to sit down at a computer with an empty command line prompt and a stack of manuals on their desk and be told to write an IDL program. Where to begin!? And the experience is especially frightening to a new user with little or no programming experience in any language, let alone IDL.
Ken Bowman has written an IDL book specifically for this user. It is intended as an introductory computer programming course for the research user with little or no training in any computer language, and it evolved from notes Ken uses in his own undergraduate IDL programming courses. It is meant to get the new IDL user analyzing and plotting data as soon as possible.
It is a narrow path he treads, because it is just as easy to offer too much detail as it is to offer too little information to the beginning user. Ken, for the most part, gets it exactly right in covering a broad selection of topics. I quibble with just two chapters. He sweeps aside the complexity of PostScript output by offering the new user two utility programs he fails to explain in the text, and his theoretical explanation of the FFT function left me gasping for breath and lamenting I hadn't paid closer attention in those long-ago math classes.
This is a book that will get you started, but probably won't answer all your questions when you turn your attention to more difficult research problems. Ken doesn't pretend it is anything other than what it is, however, and provides generous and helpful suggestions for where you can find additional information as you become ready for it. Readers already familiar with another programming language will appreciate this introduction to IDL, but might become frustrated with the slower pace and lack of specific detail on many topics.
The book has an associated web page, where you can find, among other things, the source code for all the programs mentioned in the book. Pay particular attention to the Errata section, especially if you are interested in structures in IDL. A printer glitch removed all the curly brackets from Ken's IDL code in the structure chapter and none of the examples will work as written in the book. A software problem, no doubt. (The example programs for the chapter are correct.) It serves as a reminder to me of how complex a topic software programming can be. This friendly book will be a welcome introduction to the subject for many a potential IDL programmer.

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In today's information age, scientists and engineers must quickly and efficiently analyze extremely large sets of data. One of the best tools to accomplish this is Interactive Data Language (IDLÂ), a programming and visualization environment that facilitates numerical modeling, data analysis, and image processing.IDL's high-level language and powerful graphics capabilities allow users to write more flexible programs much faster than is possible with other programming languages.An Introduction to Programming with IDL enables students new to programming, as well as those with experience in other programming languages, to rapidly harness IDL's capabilities: fast, interactive performance; array syntax; dynamic data typing; and built-in graphics. Each concept is illustrated with sample code, including many complete short programs. ÂMargin notes throughout the text quickly point readers to the relevant sections of IDL manualsÂEnd-of-chapter summaries and exercises help reinforce learningÂStudents who purchase the book are eligible for a substantial discount on a student version of the IDL software

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

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(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.

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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.

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Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)I've bought several spatial statistics books over the years and found they generally fall into one of two categories; oversimplified or cover-to-cover matrix notation, neither of which is very useful for my research. However, this book is "just right," bridging these two extremes. It briefly covers the basics of both point and areal analysis, then gives the reader the tools for more advanced (i.e., realistic) analysis. They devote a chapter to Bayesian basics, which is needed for the last 4 or 5 chapters. The last few chapters weave together a detailed discussion on a variety of hierarchical models and current published results. Most importantly this book offers quite a bit of the necessary R and Winbugs code. Although many of their examples are from the public health world, the techniques and code are easily adapted to natural resource data - my personal focus.

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Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

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Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4 Review

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(More customer reviews)The book "Modeling and Simulation with Scilab/Scicos"
is well written and understandable to
readers with the basic signal processing and programming
background.
The reader can refresh/improve the knowledge of
some basic control theory material, while at the
same time learns how to apply Scilab/Scicos at simulation and
modeling problems.
I worked with Matlab for many years before and I found
Scilab/Scicos a very powerful alternative to Matlab/Simulink
and is free!
I recommend strongly this book to any scientist/engineer that
plans to explore the benefits of the excellent open
source Scilab/Scicos environment.

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Scilab and its Scicos block diagram graphical editor, with a special emphasis on modeling and simulation tools. The first part is a detailed Scilab tutorial, and the second is dedicated to modeling and simulation of dynamical systems in Scicos. The concepts are illustrated through numerous examples, and all code used in the book is available to the reader.

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Multilevel Modeling (Quantitative Applications in the Social Sciences) Review

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(More customer reviews)Sage university papers on quantitative applications are presented as brief and inexpensive treatments of specialized topics in statistics and data analysis. Some are well worth the price, while some leave you wishing you used the money towards acquiring a full-length treatise or textbook. If you need to learn about multilevel modeling on your own, Douglas Luke's Multilevel Modeling is worth much more than its price, especially if you buy it from Amazon.com, because it is a model of compositional economy in addressing a complex idea, and of what a truly introductory textbook should be. Luke maintains focus, precision, and masterful clarity in a fashion that is rarely encountered among books which claim to be "An Introduction to ... " a topic as specialized, intricate, and novel as is multilevel statistical modeling. Luke defines the terms more lucidly than some of the most popular full-sized books which aim to introduce multilevel analysis (and which still leave the reader mired in ambiguity). The author does not attempt to impart any gratuitous complexity to his exposition and manages to integrate textual clarity with statistical notation and equations, figures, and tables which are equally clear for someone who, while familiar with concepts beyond one-variable statistics and simple linear regression and ANOVA, has never studied or engaged in this type of data analysis or research design before. You may need to proceed to thicker treatises to make a thorough analysis and find out how to use your favorite software, but if you begin with one or more of those and find the topic still unclear in its elements - either the big picture or the basic details - you will find Luke's 78 pages (including reference to data online) enlightening.

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Taking a practical, hands-on approach to multilevel modeling, this book provides readers with an accessible and concise introduction to HLM and how to use the technique to build models for hierarchical and longitudinal data. Each section of the book answers a basic question about multilevel modeling, such as, "How do you determine how well the model fits the data?" After reading this book, readers will understand research design issues associated with multilevel models, be able to accurately interpret the results of multilevel analyses, and build simple cross-sectional and longitudinal multilevel models.

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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Review

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(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.


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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

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