Applied Quantitative Methods for Trading and Investment (The Wiley Finance Series) Review

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(More customer reviews)Whereas most books on quantitative finance focus on how to price derivatives or model interest rates, this is a text on quantitative and computational methods that are about making money.
How to we forecast future prices? What is the place for artificial intelligence and neural networks? How are people using Bayesian methods and neural regressions? How can technical analysis and trend-following rules contribute to quantitative trading systems? How can new volatility and correlation models be applied (in Excel) to portfolio optimization?
These questions are answered by practitioners and academics with case studies and real-world applications. Each chapter provides a quick taste of things people are doing outside the box of your typical quant finance books. Do not expect a new philosophy or over-arching theory. This is just a book to prod half-baked ideas that might merit more consideration or to re-start one's own creative juices.

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This much-needed book, from a selection of top international experts, fills a gap by providing a manual of applied quantitative financial analysis. It focuses on advanced empirical methods for modelling financial markets in the context of practical financial applications.
Data, software and techniques specifically aligned to trading and investment will enable the reader to implement and interpret quantitative methodologies covering various models.
The unusually wide-ranging methodologies include not only the 'traditional' financial econometrics but also technical analysis systems and many nonparametric tools from the fields of data mining and artificial intelligence. However, for those readers wishing to skip the more theoretical developments, the practical application of even the most advanced techniques is made as accessible as possible.
The book will be read by quantitative analysts and traders, fund managers, risk managers; graduate students in finance and MBA courses.

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Introductory Econometrics Review

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(More customer reviews)This book was the required text for my first econometrics class. I found it unclear and ended more confused after consulting the text than before. It is very theory-based, and not very helpful if you're looking for a book about practical applications of econometric models. The only way I got through econometrics was using Pindyck and Rubinfeld's Econometrics book. Goldberger's book was short with too few examples, problems at the end of the chapter which were not discussed in the chapter. If you are buying this as a first econometrics book, don't, and if you're well versed in econometrics, then you don't need an intro book. This book caters to no level of econometric student.

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This is a textbook for the standard undergraduate econometrics course. Its only prerequisites are a semester course in statistics and one in differential calculus. Arthur Goldberger, an outstanding researcher and teacher of econometrics, views the subject as a tool of empirical inquiry rather than as a collection of arcane procedures. The central issue in such inquiry is how one variable is related to one or more others. Goldberger takes this to mean "How does the average value of one variable vary with one or more others?" and so takes the population conditional mean function as the target of empirical research.

The structure of the book is similar to that of Goldberger's graduate-level textbook, A Course in Econometrics, but the new book is richer in empirical material, makes no use of matrix algebra, and is primarily discursive in style. A great strength is that it is both intuitive and formal, with ideas and methods building on one another until the text presents fairly complicated ideas and proofs that are often avoided in undergraduate econometrics.

To help students master the tools of econometrics, Goldberger provides many theoretical and empirical exercises and, on an accompanying diskette, real micro-and macroeconomic data sets. The data sets deal with earnings and education, money demand, firm investment, stock prices, compensation and productivity, and the Phillips curve.

THE DATA SETS CAN BE FOUND HERE.

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Modern Engineering Statistics Review

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(More customer reviews)An exceptionally well written book with many useful examples, case studies, and end of chapter exercises. Balances the theoretical aspect of statistics with real world examples. One of the few books that specifically covers the application of statistics for a manufacturing / engineering environment.
A very useful resource for anyone involved in Six Sigma, quality, or engineering in a manufacturing setting.
Although not touted as a Minitab resource / reference book there are plenty of worked examples that use Minitab, which I found to be an added bonus.

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An introductory perspective on statistical applications in the field of engineering
Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering.
With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features:

Examples demonstrating the use of statistical thinking and methodology for practicing engineers

A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets

Clear illustrations of the relationship between hypothesis tests and confidence intervals

Extensive use of Minitab and JMP to illustrate statistical analyses

The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.

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Introduction to Nonparametric Regression (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Regression fitting is much in demand in diverse applications. It involves fairly elementary ideas from probability and statistics. Two groups of users come to mind: Medical professionals who must fit regression lines to make sense of and process medical data. Other users: business, sociology, biology, finance etc.
I just found this newer and up to date book. It is more to my liking than the others I came across up to now, and it includes ready-to-use software.
Idea: You want to fit lines or system of lines to data in the form (X, y) where X is a matrix; it is really a sequence of measurements of some finite selection of things you want to measure. Think of X is independent vector measurements, and y as a dependent vector variable. (Special case: If X and y are both vectors, you are looking at a set of plots in your planar coordinate system.)
You then want to test if some observed other sequence of measured numbers y depend on X in a good way; i.e., whether these dependent variables fit hypothetical linear dependencies which are prescribed by parameters b, i.e, a set of unknown numbers to be adjusted in order to get a best fit, using minimal least-square approximation. But to complicate life, there is a stochastic noise element, and it is represented by a vector e of random variables (prescribed in turn by some probability distributions, and usually assumed independent.)
So if you organize your vectors y, b, and e in column form, then you are looking at a relatively easy problem in linear algebra:
y = X b + e,
where y, b, and e are vectors of the same size, and where X is a matrix.
Oversimplification: If e = 0, there is an easy formula for a unique solution b, and gotten from demanding minimal least-square fit. (Lagrange's method will do this.) And it is in all the books. It involves a certain known and simple matrix function f applied to X. So it is b = f(X) y. And you get it ready to use from all the software you can find on the web.
If on the other hand, there is noise, i.e., if e is not 0, then you must use a little probability, but there is a lot of software where you can just input your numbers X, y, and your hypothesis about e. You still look for the best fit, i.e., the best numbers (parameters) for b, but then it is a bit more complicated than just b = f(X) y.
Naturally other hypothetical dependencies are relevant, e.g., non-linear etc. They are still prescribed by parameters but in more complicated ways. And still the approach is via demanding a minimal least-square fit to the data.
The book goes beyond the classical parametric approach. Hence the word "nonparametric" in the title!
And it is well presented and organized in this book. Review by Palle Jorgensen, October 2006.

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An easy-to-grasp introduction to nonparametric regressionThis book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features:* Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods* Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions* Mathematical equations that are accompanied by a clear explanation of how the equation was derivedThe first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts.With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics.This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.

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Step-By-Step Basic Statistics Using SAS: Exercises Review

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(More customer reviews)I have been a SAS programmer for almost 10 years now, mostly doing data cleaning, linking, and simple descriptive statistics for reports. I have recently been given work requiring more complex statistical analysis such as significance testing and logistic regression. I found this book to be a perfect guide for my situation. I have approached learning Stats for SAS via SUGI papers and other web resources which are typically geared towards professional statisticians. It was daunting to say the least. For myself, an experienced programmer needing to get my knowledge of statistics up to par, this book is invaluable. If you have never used SAS before and don't know the basics, try a different starting point like "The Little SAS Book: A Primer."

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With Step-by-Step Basic Statistics Using SAS: Exercises, you apply what you learned in the companion text, Step-by-Step Basic Statistics Using SAS: Student Guide. Using the instruction provided, you soon will be creating data sets and performing statistical analyses to investigate specific research questions. The exercise data is inspired by studies in the social and behavioral sciences, so your analyses and findings mirror real research. Each chapter presents two opportunities to explore the results of the analyses and to practice writing summary reports. For the first exercise, a complete solution is provided, including the SAS program, output, and analysis report, as well as tables and figures. For the second exercise, you supply the solution.

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Analysis of Longitudinal Data Review

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(More customer reviews)The first edition of this book was a major success as for the first time advanced methods for the use of longitudinal data were introduced. Longitudinal data (sometimes also referred to as repeated measures data) is very important in the analysis of clinical trial data. This is because many important trial endpoints are collected for each patient at several visits over the course of the trial and the study sponsor (usually the manufacturer of a drug or a device)will want to see how the measures change over time with usually the baseline measurement and the last measurement being the most important. Often they want to see in a randomized trial whether the treatment over inerest tends to perform better for the subjects taking the test treatment versus those who take the active control and/or placebo. An issue is the presence of correlation between measurements from one time point to another.
So this type of analysis is similar to time series analysis. The difference is that time series are usually studied in the situation where a single series is observed for a long time and the analyst wants to determine future behavior based on an model constructed to fit this one observed series very well. The model is intended in the time series setting to describe a stochastic process (usually a stationary process or one transformed to stationarity by removal of trends). On the other hand in longitudinal analysis each patients profile over time is usually a very short series and the collection of these series over several patients in a particular treatment group are view to come from the same stochastic process. So the data represent several short partial realizations of the stochastic process while a time series is a long, single partial realization.
Since the data differ the methods of analyses differ also. For time seies analysis the autoregressive integrated moving average models of Box and Jenkins are often employed while for longitudinal data the mixed effect linear models are often the class of models chosen. The common theme is the structure of the covariance matrix for the observations in time series and the model noise terms in the case of the linear mixed models.
Zeger and Liang were among the leaders in developing successful modelling for these data. In a series of articles they develop a restricted maximum likelihood approach to the problem of estimating the model parameters and introduce a method called GEE an acronym for generalized estimating equations. The first edition of this book was very popular in the statistical community, particularly for statisticians working in the pharmaceutical industry. Along with Peter Diggle these three authors presented in the first edition this research organized into a single book for the first time. Now there is a plethora of books some prinarily theoretical and others primarily applied. The issue of missing data is very common to this type of data particularly when the data come from a clinical trial. The research of Molenberghs and Verbeke, covered by them in some repeated measures books, has shown these models to be among the most useful for handling missing data in realistic ways.
This second edition of this book has even greater coverage of topics and includes a fourth author Patrick Heagerty. Each of the four authors are skill research statisticians who specialize in biostatistics and particularly longitudinal data. While today there are many books to choose, this text continues ot be among the best.

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The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values.

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Presurgical Psychological Screening in Chronic Pain Syndromes: A Guide for the Behavioral Health Practitioner Review

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(More customer reviews)This book starts at the beginning and has all of the necessary steps. Now if we could only get insurance companies to read the book and see the importance of this work!

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Pain is an unfortunate daily experience for many individuals. Chronic pain -- lasting six or more months -- is suffered by approximately 30% of the population in the United States. These individuals wake up, function during the day and go to sleep, trying to keep pain at a minimum while, at the same time, maintaining some quality of life. They may make frequent visits to the doctor and the pharmacy. When they find relief, it is usually short-lived and comes at a cost such as dependence on narcotic medications or complete limitation of activity. Pain often becomes the central point of their existence. This practice guide describes an approach to psychological evaluation of the chronic pain patient who is being considered for surgery. A large body of research is accumulating which demonstrates that the outcome of surgical procedures aimed at chronic pain relief can be strongly influenced by psychological and emotional factors. This approach, termed "presurgical psychological screening" (PPS) uses interview and testing techniques to identify emotional, behavioral, and psychosocial difficulties which have been demonstrated to negatively impact surgical outcome. Studies show that even patients with clearly identifiable pathophysiology may respond poorly to surgery, due to issues such as pain sensitivity, medication dependence, rewards for pain behavior and personality style. Thus, some insurance carriers, rehabilitation nurses and state worker's compensation systems are encouraging, or even requiring, presurgical psychological screening in cases of surgery designed to relieve chronic pain. The first to present a comprehensive, unified approach to PPS in chronic pain syndromes, this text is designed to provide the behavioral health practitioner, as well as the trainee, with all the tools and information necessary to conduct PPS evaluations. It identifies a multitude of risk factors for poor surgical outcome and reviews research associated with each risk factor. Hands-on techniques for eliciting information from the patient about risk factors is also detailed. Toward this end, the practice guide also contains a number of forms and session outlines which can be directly utilized, or which can be altered to fit readers' needs. Models for weighing and combining surgical outcome risk factors are also provided. Thus, practitioners are able to reach valid and reliable predictions of surgical results. Finally, the text provides outlines of psychological interventions which can facilitate surgical outcome as well as surgical treatment alternatives. Upon completion of this practice guide, readers should be able to begin providing PPS evaluations which are scientifically valid, clinically sound, and which result in significant overall improvement in the treatment of chronic pain syndromes.

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Interpolation of Spatial Data: Some Theory for Kriging (Springer Series in Statistics) Review

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(More customer reviews)Michael Stein got his Ph.D. in Statistics from Stanford University under the direction of Paul Switzer. I also studied at Stanford years earlier and also learned about kriging from Switzer. Kriging is a very popular technique for interpolation of spatial data between measurement points. It is an optimal linear technique when the spatial covariance structure is known. It has many practical applications to pollution data, geological data etc. Stein develops the theory as far as he can for the case when the covariance structure is unknown and must be estimated based on the measurement data.
The theoretical development requires some advanced mathematical knowledge on the part of the reader including advanced probability, Fourier analysis and Hilbert spaces. The second order properties of random fields and results on Gaussian measures needed for the development of key results are covered in Chapter 2. Those interested in the practical aspects of kriging can omit the proofs and just concentrate on the results. Chapter 6 provides important practical information.
Although difficult to digest, a careful reading of the book will provide insight into what is good and what is bad about the way kriging is commonly implemented. The bootstrap approach to assessing the accuracy of kriging predictions is briefly discussed in section 6.8 page 202.
This text concentrates on Stein's development of fixed domain asymptotics. It does not provide a broad overview of kriging. That can be found in Noel Cressie's book. It also does not deal with other aspects of interpolation such as nonlinear interpolation, estimation for non-Gaussian processes or the connections with splines.
Nevertheless this is a landmark text that should be on the shelf of any statistician interested in spatial data.

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Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.

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Longitudinal and Panel Data: Analysis and Applications in the Social Sciences Review

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(More customer reviews)This is a very good book on panel data analysis. The author nicely summarized the key ideas about panel data from a few academic fields (biostatistics, econometrics, social science, and general statistics). The analysis of panel data could be confusing because different people in different fields could call the same thing by different names. People in econometrics sometimes ignore what is well developed in biostatistics, and vice versa. The author built the bridge among all these fields using an unified language.
This is a book about applied statistics. Theories and mathematics are covered in the appendices at the end of each chapter and the end of the book. A lot of examples from economics and social science are demonstrated in the book. Exercises are well organized and used to provide additional insights.
Highly recommend for applied statisticians.

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Focusing on an analysis of models and data that arise from repeated observations of a cross-section of individuals, households or firms, this book also covers important applications within business, economics, education, political science and other social science disciplines.The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented social science graduate students as well as individual researchers.He emphasizes mathematical and statistical fundamentals but also demonstrates substantive applications from across the social sciences. These applications are enhanced by real-world data sets and software programs in SAS and Stata.

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Adaptive Filters Theory and Applications Review

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(More customer reviews)As a course instructor, I found adaptive filters a very helpful book for teaching. It is also an easy to read book, as a self-study. Having used the previous books in the market, I found this a lot easier to teach. My students seem to understand much better. The very nicely designed end of the chapter problems are very helpful in enhancing the students understanding of the basic concepts. Farhang-Boroujeny has a very special view towards the fundamental concept of convergence; the significance of the power spectrum of filter input on convergence behavior. He starts injecting this concept right from the chapter on Wiener filters and carry this through out the book. Chapters 6 and 12, giving the basic concepts of LMS and least squares, are really nice and very instructive. Chapter 7 is very enlightening in understanding the basic concepts of the convergence and its relationship with the power spectrum of filter input. Chapter 8 has made the difficult topic of frequency domain adaptive filters very easy to follow. It also contains some unpublished work which I found very interesting. Other chapters also bring to the attention new views of adaptive filters. I am sure that the students and new researchers enjoy this book and will learn all important concepts of the adaptive systems and filters from this very well written book.
Congratulations to Dr. B Farhang-Boroujeny!

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This enlightened engineering approach to the study of adaptive filters employs MATLAB® computer simulations to clarify theoretical results. A highly accessible text, Adaptive Filters elucidates the concept of convergence and provides many application examples. The comprehensive coverage includes the theory of Wiener filters, eigenanalysis, the complete family of LMS-based algorithms, recursive least-squares and a new treatment of tracking. Features include:Accompanying diskette containing the MATLAB programs used throughout the book and providing an insight into adaptive filtering conceptsEnd-of-chapter exercises designed to extend results developed in the text and to sharpen the readers skill in theoretical developmentMATLAB-based simulation problems which will enhance understanding of the behaviour of different adaptive algorithmsThorough treatment of transform domain, frequency domain and subband adaptive filtersSection on eigenanalysis presenting the essential mathematics for the study of filtersA valuable student resource and an essential technical reference for signal processing engineers in industry, Adaptive Filters presents a broad subject overview with emphasis on new developments and popular applications.

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Classification and Regression Trees Review

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(More customer reviews)In 1984 Brieman, Olshen, Friedman and Stone published this book and produced a software product called CART that made tree classification popular. These algorithms were very useful in medical applications and the book illustrated some simple success stories particularly ones from Richard Olshen's experience working in the Medical School at UC San Diego.
Olshen and Gordon did some of the work on the asymptotic theory of recursive partitioning that made the methodology credible to the statistical research community. The methods began to be applied to pattern recognition problems and also to the development of expert systems. Today data miners use these tools.
These ideas goes back a lot further than these authors. However, previous attempts at recursive partitioning algorithms tended to grow trees with too many terminal nodes. These authors introduced two important ideas. One was to grow the trees overly long and then prune them back. The second was to continually use cross-validation to evaluate the trees.
This book is still very valuable 24 years after it was first published. It is also readible by general audiences for the most part. It now stands as a classic text on the subject of classification and regression trees. There are also books that followed in its footsteps and other places where tree structure comes into play.

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The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

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Obesity Epidemiology Review

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(More customer reviews)Why would adipocyte/adipose tissue secretory products predict weight gain, when as a whole, they are the consequence, not the cause of acquired obesity? Why would insulin resistance predict weight gain when weight gain, under the lifestyle model of type 2 diabetes onset, is the cause - not the consequence of target tissue insulin resistance? Why not explain the role of adipogenesis fully and completely? And if insulin resistance (at the adipocyte) occurs along (side-by-side) with some measure of heritable obesity due to genetic/congenital/programming factors, acquired obesity per se would probably not be concerning. Why is there not a single paragraph on the treatment of adipocyte cellularity and adipocyte size? Why is there no discussion of adipocyte hypertrophy (the cause and consequence of adipocyte size) or hyperplasia (the cause and consequence of adipose cellularity) - That is, the proliferation and differentiation of new adipocytes in adults under a variety of circumstances (eg, some oral anti-diabetic meds?) Why on earth are the ligand agonists (TZD's) of PPAR-gamma only mentioned in relation to "fat redistribution" when in fact, PPAR-gamma is considered a key regulator in adipogenesis? Why not discuss PPAR-gamma fully and then provide information on ligand activation. This very incomplete discussion makes it appear as if visceral fat magically moves to subcutaneous regions, and voila, insulin sensitivity returns! Why is insulin sensitivity improved? Why no discussion? Why is insulin resistance explicitly mentioned as a problem only for liver and skeletal glucose uptake, when the majority of excess energy uptake occurs at the adipocyte? I could go on and on, but suffice to say, in my humble opinion, this book seriously lacks the most basic anatomic, physiologic, and biologic foundation to intelligently address, research, and interpret the problem(s) of obesity in populations. Why so many important aspects of the obesity discussion were ignored or left out is unclear and concerning. It makes no sense. For new students to the topic, this book will not bring them closer to understanding obesity. It will definitely confuse, more than enlighten, but they probably won't even know. For students who are up to date on the obesity literature, they will most certainly feel as I do.

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During the past twenty years there has been a dramatic increase in obesity in the United States.An estimated thirty percent of adults in the US are obese; in 1980, only fifteen percent were.The issue is gaining greater attention with the CDC and with the public health world in general. This book will offer practical information about the methodology of epidemiologic studies of obesity, suitable for graduate students and researchers in epidemiology, and public health practitioners with an interest in the issue. The book will be structured in four main sections, with the majority of chapters authored by Dr. Hu, and some authored by specialists in specific areas.The first section will consider issues surrounding the definition of obesity, measurement techniques, and the designs of epidemiologic studies. The second section will address the consequences of obesity, looking at epidemiologic studies that focus on cardio-vascular disease, diabetes, and cancerThe third section will look at determinants obesity, reviewing a wide range of risk factors for obesity including diet, physical activity and sedentary behaviors, sleep disorders, psychosocial factors, physical environment, biochemical and genetic predictors, and intrauterine exposures.In the final section, the author will discuss the analytical issues and challenges for epidemiologic studies of obesity.

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Identification for Prediction and Decision Review

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(More customer reviews)The very detailed table of contents below should give you an idea of what this interesting book is about. Good food for thought for both social scientists and statisticians.
TABLE OF CONTENTS
Preface
Introduction
The Reflection Problem
The Law of Decreasing Credibility
Identification and Statistical Inference
Prediction and Decisions
Coping with Ambiguity
Organization of the Book
The Developing Literature on Partial Identification
Prediction with Incomplete Data
Conditional Prediction
Predicting Criminality
Probabilistic Prediction
Estimation of Best Predictors from Random Samples
Extrapolation
Predicting High School Graduation
Best Predictors under Square and Absolute Loss
Nonparametric Regression Analysis
Word Problems
Missing Outcomes
Anatomy of the Problem
Bounding the Probability of Exiting Homelessness
Means of Functions of the Outcome
Parameters That Respect Stochastic Dominance
Distributional Assumptions
Wage Regressions and the Reservation-Wage Model of Labor Supply
Statistical Inference
Interval Measurement of Outcomes
Jointly Missing Outcomes and Covariates
Convergence of Sets to Sets
Instrumental Variables
Distributional Assumptions and Credible Inference
Missingness at Random
Statistical Independence
Equality of Means
Inequality of Means
Imputations and Nonresponse Weights
Conditioning on the Propensity Score
Word Problems
Parametric Prediction
The Normal-Linear Model of Market and Reservation Wages
Selection Models
Parametric Models for Best Predictors
Minimum-Distance Estimation of Partially Identified Models
Decomposition of Mixtures
The Inferential Problem and Some Manifestations
Binary Mixing Covariates
Contamination through Imputation
Instrumental Variables
Sharp Bounds on Parameters That Respect Stochastic Dominance
Response-Based Sampling
The Odds Ratio and Public Health
Bounds on Relative and Attributable Risk
Information on Marginal Distributions
Sampling from One Response Stratum
General Binary Stratifications
Analysis of Treatment Response
The Selection Problem
Anatomy of the Problem
Sentencing and Recidivism
Randomized Experiments
Compliance with Treatment Assignment
Treatment by Choice
Treatment at Random in Nonexperimental Settings
Homogeneous Linear Response
Perspectives on Treatment Comparison
Word Problems
Linear Simultaneous Equations
Simultaneity in Competitive Markets
The Linear Market Model
Equilibrium in Games
The Reflection Problem
Monotone Treatment Response
Shape Restrictions
Bounds on Parameters That Respect Stochastic Dominance
Bounds on Treatment Effects
Monotone Response and Selection
Bounding the Returns to Schooling
The Mixing Problem
Extrapolation from Experiments to Rules with Treatment Variation
Extrapolation from the Perry Preschool Experiment
Identification of Event Probabilities with the Experimental Evidence Alone
Treatment Response Assumptions
Treatment Rule Assumptions
Combining Assumptions
Planning under Ambiguity
Studying Treatment Response to Inform Treatment Choice
Criteria for Choice under Ambiguity
Treatment Using Data from an Experiment with Partial Compliance
An Additive Planning Problem
Planning with Partial Knowledge of Treatment Response
Planning and the Selection Problem
The Ethics of Fractional Treatment Rules
Decentralized Treatment Choice
Minimax-Regret Rules for Two Treatments Are Fractional
Reporting Observable Variation in Treatment Response
Word Problems
Planning with Sample Data
Statistical Induction
Wald's Development of Statistical Decision Theory
Using a Randomized Experiment to Evaluate an Innovation
Predicting Choice Behavior
Revealed Preference Analysis
Revealing the Preferences of an Individual
Random Utility Models of Population Choice Behavior
College Choice in America
Random Expected-Utility Models
Prediction Assuming Strict Preferences
Axiomatic Decision Theory
Measuring Expectations
Elicitation of Expectations from Survey Respondents
Illustrative Findings
Using Expectations Data to Predict Choice Behavior
Measuring Ambiguity
The Predictive Power of Intentions Data: A Best-Case Analysis
Measuring Expectations of Facts
Studying Human Decision Processes
As-If Rationality and Bounded Rationality
Choice Experiments
Prospects for a Neuroscientific Synthesis
References
Author Index
Subject Index
If you want to gain perspective into Charles Manski's thinking check his other book - "Identification Problems in the Social Sciences". Manski is an original thinker that deserves a lot of attention from all those involved in the measurement of social phenomena.

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This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements.

Building on the foundation laid in the author's Identification Problems in the Social Sciences (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior.

Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.

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Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner Review

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(More customer reviews)A good book to understand decision trees using SAS e-miner. I wish it could have more literature on the splitting algorithms i.e. Gini, Entropy, Chisquare.
The book along with SAS data mining material or Data Mining book by Larose is a good resource to understand Decision Tree.

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Using SAS Enterprise Miner, this book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. Examples show how various aspects of decision trees are constructed, how they operate, how to interpret them, and how to use them in a range of predictive and descriptive applications. The examples are drawn from the areas of purchase behavior, risk assessment, and business-to-business marketing. This book also describes the various disciplines that contributed to the development of decision trees and how, even today, decision trees can be used as a form of machine intelligence. Examples of using and interpreting graphic decision trees as executable rules are provided. The target audience includes analysts who have an introductory understanding of data mining and who want to benefit from a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining.

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Primal-Dual Interior-Point Methods Review

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(More customer reviews)Excelent book, a must-have for everyone that has interest on the subject.

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In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work.The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issues relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems.

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Evidence-Based Medicine: A Framework for Clinical Practice Review

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(More customer reviews)Daniel Friedland, MD, is the Founder of SuperSmartHealth, [...]/about . In "Evidence Based Medicine: A Framework for Clinical Practice", he provides a foundation which can enable both the busy Clinician, and the Healthcare System, to undertake practical application of Evidence Based Medicine principles. Doctor Friedland outlines methods to frame questions and find, evaluate, and apply medical literature searches which support informed decision-making, optimal Patient-care, & systemic-value. Highly recommended!

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This book is a clinically oriented introduction to the new, emerging field of evidence-based medicine.

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Statistical Analysis Quick Reference Guidebook: With SPSS Examples Review

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(More customer reviews)I just received my copy of this book, and it looks great! If I were teaching a research methods class, I'd adopt this as a required text. In fact, I used the book today when one of my college students asked how to report the results of a test in an academic paper. I quickly turned to the right section and consulted the "How to report..." section. Excellent!

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Statistical Analysis Quick Reference Guidebook: With SPSS Examples is a practical "cut to the chase" handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, authors Alan C. Elliott and Wayne A. Woodward provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results, and reporting outcomes.

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