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Technical Reviewers Book List

Bayesian Estimation of Item Response Theory Models Using SAS PROC MCMC
By Clement A. Stone and Xiaowen Zhu

Recently, routines have become available in SAS software to implement general Bayesian estimation methods (PROC MCMC). Use of SAS has several significant advantages over other available programs such as WinBUGS (Spiegelhalter, Thomas, & Best 2000): (1) It is commonly used by researchers across disciplines; (2) it provides a robust programming language that extends the capability of the program—in particular, capability for model checking; and (3) it shows increased performance and efficiency. The purpose of this book is to illustrate Bayesian estimation and model checking of item response theory models that are of interest to psychometricians, scale developers, and individuals responsible for implementing assessment programs. The structure of the book is example-driven, with sections presenting code for estimating particular models and results illustrating convergence diagnostics and inference for parameters, as well as results that can be used specifically by scale developers.

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SAS Optimization Essentials: A Step-by-Step Guide for New Users of PROC OPTMODEL
By T. Glenn Bailey

T. Glenn Bailey’s SAS Optimization Essentials: A Step-by-Step Guide for New Users of PROC OPTMODEL shows you how easy it is to employ optimization using PROC OPTMODEL, while simultaneously leveraging the overall SAS environment. With this book you will quickly bridge the gap between optimization formulation and solution implementation, by effectively translating mathematical models into SAS using PROC OPTMODEL. Part 1 introduces essential PROC OPTMODEL programming, declaration, and procedural syntax; data I/O; and extensive examples of linear programming and mixed-integer programming problems. Part 2 provides advanced examples of the topics covered in part 1, and reviews how PROC OPTMODEL can be used with SAS/GRAPH and the Macro Programming Language.

SAS Optimization Essentials is unique in that it focuses on the essential statements and syntax necessary to run a majority of optimization problems, and arranges the presentation of PROC OPTMODEL’s procedures and syntax to follow the process of mathematically formulating an optimization problem. Loaded with examples, the book teaches the reader how to "think in PROC OPTMODEL" the same way they already "think in mathematical terminology" when considering an optimization problem. If you are a SAS user and want to use SAS for optimization, this is the book for you.

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Time Series Modeling Using PROC VARMAX
By Anders Milhøj

Aimed at econometricians who have completed at least one course in time series modeling, this example-driven book will present the reader with an up-to-date version of the time series analytical possibilities that SAS offers. Today, estimations of model parameters are performed in a split second. For this reason, working through the identifications phase in order to find the correct model is unnecessary. Instead, several competing models can be estimated, and their fit can be compared instantaneously. Consequently, for time series analysis, most of the Box and Jenkins analysis process for univariate series is now obsolete. The former days of looking at cross-correlations and pre-whitening are over, because distributed lag models are easily fitted by an automatic lag identification method. The same goes for bivariate and even multivariate models, for which PROC VARMAX models are automatically fitted. For these models, other interesting variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and error correction are easily treated by PROC VARMAX. One particular problem with multivariate modeling is that it includes many parameters, making parameterizations unstable. This instability can be compensated for by application of Bayesian methods, which are also incorporated in PROC VARMAX. Volatility modeling has now become a standard part of time series modeling, because of the popularity of GARCH models. These models are estimated both in a number-univariate version and in a multivariate version by PROC VARMAX. This feature is especially interesting for financial analytics in which risk is a focus. Readers of this book who are analyzing a time series for the first time will find PROC VARMAX easy to use; readers who know more advanced theoretical time series models will discover that PROC VARMAX is a useful tool for advanced model building.

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