Book Description
This text/reference provides a broad survey of aspects of model-building and statistical inference. Presents an accessible synthesis of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.
Download Description
This text/reference provides a broad survey of aspects of model-building and statistical inference. Presents an accessible synthesis of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.
Customer Reviews:
Good Text: Rigorous and Theoretical but Clear.......2007-01-09
I am a Chemist Phd working in the oil refining industry and often use statistical tools to model plant process and laboratory data. this text is not for practical uses but is to be considered a very rigorous and in depht introduction to nonlinear regression theory. Even if quite long and detailed is still clear and not difficult to understand. if you have time to devote to the principles and not jump to solutions this is a very good advanced text
excellent coverage by accomplished authors.......2001-05-05
I have recently reviewed for amazon the texts by Gallant and the one by Bates and Watts. This text was written by Seber and Wild, two accomplished statisticians and experienced authors. This volume is of the same high caliber as those texts and deserves mention. It is a longer text that overlaps on many topics with the other two books, deliberately neglects some areas that were well covered by Gallant (Gallant's book came out in 1987 and this one in 1989) and hits some topics not covered by either of the other two books.
Bootstrap methods are neglected probably because the value of the bootstrap for standard error estimation in nonlinear models was not yet appreciated in 1989.
Chapters 1 and 2 provide good introductory material similar to the other texts. Chapter 1 deals with the models (linear and nonlinear) and Chapter 2 provides the basic estimation techniques. In addition to the standard material on least squares, generalized least squares and maximum likelihood, the authors also cover quasi-likelihood, linear approximations, robust estimation and Bayesian methods. Box - Cox transformations and the issue of variance heterogeneity are also treated in Chapter 2.
As they remark in the preface, they avoid much of the econometric theory and asymptotic theory that is well covered in Gallant's book.
Chapter 3 deals with important practical issues including the convergence properties of the iterative procedures (important for nonlinear models but a non-issue in linear models), ill-conditioning and identifiability (important issues for both linear and nonlinear models).
Chapter 4 deals with curvature issues and covers much of the original work of Bates and Watts with many references to those authors. Oddly though, there is no mention of the Bates and Watts text. Both books were published by Wiley around the same time with Bates and Watts appearing in 1988 and Seber and Wild in 1989. Perhaps the Seber and Wild book went to the publisher before the Bates and Watts book came out (their preface has a May 1988 date).
Important and interesting topics covered in this book but not the others include models with time dependent errors, detailed treatment of growth models, compartmental models, multiphase and spline regresions and error-in-variables models. They also devote a whole chapter to software issues (very interesting and practical but probably mostly outdated).
Good for a graduate statistics course or for a research reference source. Has lots of material and references but lacks homework problems.
Excelent book on nonlinear regression!.......2000-04-05
This book covers the whole theory of nonlinear regression. I think it is essential both for students of statistics and for scientists, not only as a study book but also as a reference book. I recommend it to those who already have had an introductory course on the subject and need to go deeper into it.
Book Description
This encyclopedic, detailed exposition spans all the steps of one-period allocation from the foundations to the most advanced developments.
Multivariate estimation methods are analyzed in depth, including non-parametric, maximum-likelihood under non-normal hypotheses, shrinkage, robust, and very general Bayesian techniques. Evaluation methods such as stochastic dominance, expected utility, value at risk and coherent measures are thoroughly discussed in a unified setting and applied in a variety of contexts, including prospect theory, total return and benchmark allocation.
Portfolio optimization is presented with emphasis on estimation risk, which is tackled by means of Bayesian, resampling and robust optimization techniques.
All the statistical and mathematical tools, such as copulas, location-dispersion ellipsoids, matrix-variate distributions, cone programming, are introduced from the basics. Comprehension is supported by a large number of figures and examples, as well as real trading and asset management case studies.
At symmys.com the reader will find freely downloadable complementary materials: the Exercise Book; a set of thoroughly documented MATLAB
® applications; and the Technical Appendices with all the proofs. More materials and complete reviews can also be found at symmys.com.
Customer Reviews:
Not for the faint-hearted.......2007-01-31
A great book if you have a strong mathematical background. But the question of asset allocation is bedevilled by mathematics which is too strong to support the weak data supplied by the markets in which we invest.
Unless this weak data is properly integrated into the asset allocation process, an area which Meucci spends too little time on, then the users of quantitative procedures will continue to be disappointed.
Book Description
The classic text is Psychometric Theory. Like the previous edition, this text is designed as a comprehensive text in measurement for researchers and for use in graduate courses in psychology, education and areas of business such as management and marketing. It is intended to consider the broad measurement problems that arise in these areas and is written for a reader who needs only a basic background in statistics to comprehend the material. It also combines classical procedures that explain variance with modern inferential procedures.
Customer Reviews:
Purchase the 2nd Edition.......2007-06-15
As have several other people who have reviewed this book I would suggest purchasing the 2nd edition. I have both of them sitting side by side on a shelf and use the 2nd edition much more than I use the 3rd one. It is not written in as clear a manner as the 2nd edition and sought to expand beyond psychometric theory which might have watered it down some. I was surprised that the reviewer who wrote that the 3rd edition is longer than the 2nd is correct as the 2nd is thicker - but it does have fewer pages.
I would give it -5 starts if possible.......2007-01-29
This is not really a book. It's more like a composition notebook with definitions of terms on the even-number pages and, you bet, ruled spaces on the odd-number side. So this 103-page "book" is more like 51 pages (translation - you get only 50% for what you paid). Another reviewer said it's more like a Cliff Notes of the original book. Even that is too flattering. It's not even a Cliff Notes because what are on the left side (the even-number pages) are just definitions of terms taken from the textbook. There is no structure or organziation. So you cannot even tell how those terms relate to each other. I was actually looking for a Cliff Notes of Psychometric Theory, so it doesn't bother me it's not the real textbook. But in this form this book is totally useless. I could teach a monkey to copy all the terms and definitions from the textbook and publish a book like this and make some easy money. An outrageous rip-off! Stay away from all books in this Cram101 series. If you "cram" like this you probably will fail all your exams! I'm an college instructor myself and I will never test my students on how good they remember defintions in the book. At the very least learning is about understanding of the relationships between the terms.
THIS LISTING IS MISREPRESENTATIVE.......2006-09-03
I ordered this book, thinking that it was the text book (as the title of the listing suggests). My mistake not to read all the way to the bottom of the page where the true nature of this publication is finally revealed, but SHAME ON AMAZON for listing it this way. There is nothing in the actual listing of this book that suggests that it is merely a "cliffs notes" guide to the book itself. I now have to scramble to find the correct book. When you're looking for textbooks, most of us look at the title, the author and the correct edition, which this listing contains at the very top. Thanks, Amazon, for taking advantage of those of us who are too busy to read the fine print!
comprehensive but jumbled.......2006-03-16
This is one of the landmark Measurement books for Psychologists. It does present a relatively comprehensive treatment of the issues facing researchers when developing measures. Unfortunately, the style of writing used in the book makes it exceedingly difficult for students to extract the useful information from the chapters. Specifically, the chapters are not particularly well organized - particularly the ones with fewer equations in them - often jumping back and forth between topics rather than presenting them more systematically. Furthermore, the prose explaning concepts and equations is basically written in an overly complex and sometimes cryptic style more appropriate for mathematicians and psychologists from the 1950's than for graduate students or modern consumers. I only bought the book to augment the graduate level measurement class that I teach and despite the fact that I have a solid background in mathematics, I grown inwardly every time I have to pick up a chapter in this book and read it.
get the 2nd edition.......2005-02-06
I am in a management PhD program and we have to read this book for our required class in psychometric theory. I totally agree with one of the other reviewers that almost no term is clearly defined by Nunnally and Bernstein in this 3rd edition. The book goes on and on and on talking about validity, reliability, scaling, ... without defining any single term in a concise manner. It is very frustrating!
So, my suggestion for everyone is to get the 2nd edition. I read it and was happily surprised. Nunnally is great, Ira Bernstein messed the 3rd edition up (Nunnally died a while ago and Bernstein was responsible for the writing of this edition). The previous edition is much, much shorter and has better organized chapters that go right to the point (well, relatively speaking ). In addition, I recommend several short Sage books (e.g., factor analysis from Kim and Mueller), which are much clearer.
In any case, this book or better the 2nd edition, is a must have for any social science researcher (or wanna be researcher ).
Customer Reviews:
Excellent (hardcover) textbook.......2006-01-04
I agree with A. Noushin's July 26, 2004 review on Steven M Kay's 'Modern Spectral Estimation: Theory and Application' textbook. This textbook helped some of us at Lockeheed Missiles & Space Co. solve some spectral questions that others had worked on and not answered.
I recommend this textbook for -all- electrical engineering students in hope that they will understand there is more than one way to analyse ones frequency components.
For more visit my website at http://digitalCalculus.com/page/593045
A very nice book.......2004-07-26
My comments here are for the hard cover version (I do not have the paper back version). This is one of the best books on the subject of spectral estimation that I have seen. The book is easy to follow yet comprehensive and detailed. You could read sections as they are without getting confused. There are a lot of interesting problems at the end of each chapter. It is a good book for self study as well as a reference. The book is divided into two sections: Basic Methods, Advanced Concepts. The first section covers the followings: Introduction, Review of matrix theory, Review of probability, Classical methods (periodogram, windowing etc.), Parametric Modeling, Auto-regressive Estimation, MA Methods, Minimum Variance Estimation. Part two contains: Sinusoidal Parameter Estimation (MLE, Eigen-analysis, Cramer-Rao bounds, MUSIC etc.), Multi-channel Spectral Estimation, Two Dimensional Spectral Estimation, and finally one chapter on applications such as Bandwidth Compression, Beam-forming/Direction of Arrival and more.
The explanations are very clear and to the point. The book contains two test data sets (Real and Complex). There are program listings (FORTRAN) for most of the methods discussed. The listings contain the input and output samples which are very useful for testing your own programs. The book comes with a disk that contains the programs but my version is on an old 5 ½ inch IBM disk and my computer does not have the drive to read it. The book is 543 pages and contains 16 chapters, many appendences and each chapter has reference listings to other books and papers.
Average customer rating:
- Excellent text
- Well-organized, readable, beautiful
- Wonderful and insightful
- Linear Estimation from A to Z.
|
Linear Estimation
Thomas Kailath ,
Ali H. Sayed , and
Babak Hassibi
Manufacturer: Prentice Hall
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Fundamentals of Adaptive Filtering
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ASIN: 0130224642 |
Customer Reviews:
Excellent text.......2005-09-25
This is an excellent text that covers estimation theory from a modern point of view. It will be especially interesting to anyone with a graduate degree in physics because Kailath, et al derive the theory of linear estimation from a point of view very similar to that of modern quantum mechanics - they even use similar bra/ket notation!
Basic and advanced statistical mathematics is somewhat an implied prerequisite for understanding this text. From what I have seen, I honestly find nothing negative to critique - its probably one of the best technical textbooks I have in my large library.
Well-organized, readable, beautiful.......2002-10-10
I came to this book with a need to become familiar with Kalman filters. I've read the first two chapters so far with great pleasure. Professor Kailath develops the material beautifully; his profound mastery of the field is evident in every paragraph. The material is concentrated, but is presented in a highly readable compelling style. The reader is expected to be comfortable with the basics of linear systems theory, probability, and matrix analysis, although extensive appendices provide the necessary background.
Wonderful and insightful.......2001-09-18
This is one of the best engineering textbooks I have read, period. Although the subject matter is not for the faint-hearted, the authors' attention to pedagogical details shine throughout (repetition is the key to learning). The Kalman filter is introduced naturally as a consequence of a general framework for obtaining the best linear estimator of a random variable given others (earlier observations), and the geometric intuition is stressed repeatedly.
No important issue is omitted, including a very complete treatment of numerical issues and fast algorithms. My only gripe is with the assumption that all model parameters are KNOWN; in other words, the important aspect system identification (parameter estimation, learning, or whatever you call it in your field) is left to other textbooks.
Moreover, and no minor accomplishment, is the amazingly small number of typographical errors (at least up to where I have read so far; a bit over half the book), which is remarkable given the dense mathematical contents.
All in all, I would give it 6 stars if possible. Everything is there: it transmits a deep intuition for the matter, a places it in its historical context through interesting and amusing notes; it leaves the reader fulfilled but not overwhelmed.
Linear Estimation from A to Z........2001-02-06
Kailath, Sayed, and Hassibi do an excellent job of explaining what is a fairly complicated subject. This book is best-suited for scholars who desire a deep understanding of estimation theory. Engineers who want to quickly understand how to implement a Kalman Filter might be better off buying Adaptive Filter Theory by Simon Haykin.
The first chapter provides a good overview of the book, although it makes the most sense once the subject matter of the rest of the book has been digested a bit. A consistent framework emphasizing innovations (or the new information which appears at any iteration) is used throughout the book, and both continuous and discrete-time techniques for stochastic estimation are given nearly equal treatment, although the real-world engineer is likely to be interested in the latter.
Professor Kailath's articulate nature and knack for the clever anecdote or one-liner shines throughout the book, making it, while very mathematical in nature, quite readable for the motivated student.
Average customer rating:
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Random Iterative Models (Stochastic Modelling and Applied Probability)
Marie Duflo
Manufacturer: Springer
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ASIN: 3540571000 |
Book Description
The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multiaccess broadcast channels, self-learning of neural networks ...). This book provides an up-to-date view of a wide range of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning ...Mathematicians (researchers and also students) and engineers will find here a self-contained account of many approaches to those theories.
Book Description
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
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From Product Description to Cost: A Practical Approach: Volume 1: The Parametric Approach (Decision Engineering)
Pierre Marie Maurice Foussier
Manufacturer: Springer
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ASIN: 185233973X |
Book Description
Parametric cost estimating, when properly used, is a very powerful and cost effective technique. Unfortunately, this technique is often misunderstood and rejected by many potential users for whom it could be a beneficial tool. The development of an agreement on how to use it and how to recognise its limitations is a major benefit to the cost estimating community.
From Product Description to Cost: A Practical Approach:
• presents different ways of parametrically forecasting costs, and the advantages and disadvantages of these methods by using real examples from the mechanical, software and building industries;
• discusses most of the mathematical procedures which are useful for parametrically forecasting costs;
• introduces the judgement needed to audit the ways in which these forecasting techniques are used, firstly as a process, and secondly as a tool to generate estimates.
Volume 1: The Parametric Approach contains four parts. Over the course of this volume, cost estimating is introduced; the preparation of data before utilization is discussed; the basic concepts of ‘general’ cost estimating models are examined; the use of cost models is considered and finally, risk analysis (as it can be used during parametric cost estimating) is introduced.
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Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques
Jeffrey T. Spooner ,
Manfredi Maggiore ,
Raúl Ordóñez , and
Kevin M. Passino
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ASIN: 0471415464 |
Book Description
- Includes a solution manual for problems.
- Provides MATLAB code for examples and solutions.
- Deals with robust systems in both theory and practice.
Download Description
Includes a solution manual for problems.
* Provides MATLAB code for examples and solutions.
* Deals with robust systems in both theory and practice.
Average customer rating:
- A must-have
- Old is GOLD! Remarkable collection of topics and problems...
- A Classic Text
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Detection, Estimation, and Modulation Theory. Part I: Detection, Estimation, and Linear Modulation Theory
H. L. Van Trees
Manufacturer: John Wiley & Sons Inc
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ASIN: 0471899550 |
Book Description
- Highly readable paperback reprint of one of the great time-tested classics in the field of signal processing
- Together with the reprint of Part III and the new Part IV, this will be the most complete treatment of the subject available
- As imperative today as it was when it originally published
- Has important applications in radar, sonar, communications, seismology, biomedical engineering, and astronomy
- Includes section summaries, examples, and a large number of problems
Download Description
* Well-known authority, Dr. Van Trees updates array signal processing for today's technology
* This is the most up-to-date and thorough treatment of the subject available
* Written in the same accessible style as Van Tree's earlier classics, this completely new work covers all modern applications of array signal processing, from biomedicine to wireless communications
Customer Reviews:
A must-have.......2007-05-19
If you are looking for a well-organized, comprehensive, detailed book in the subject of detection and estimation, this is the book you must have. The author explains every subject very clearly, elaborates on important points, and, most importantly, supports the theoretical basis with many useful examples. It seems that Prof. Van Trees used his experience as a professor teaching this subject very effectively. In the book, there is nothing unclear, nothing too complicated to understand.
Old is GOLD! Remarkable collection of topics and problems..........2006-04-05
Van Trees, Part I (together with Wozencraft/Jacobs' Principles of Communication Engineering, and Gallager's Information Theory) is a must read to establish a solid background in detection/estimation theory and form connections to applications such as communications engineering and information theory.
Although most recent graduate education uses Kay's book (which is also a remarkable book), there are still a lot of details in which Van Trees, Part I excels. Especially, the exercise problems are actually lectures by themselves, and first time reader is encouraged at least to look at selected problems listed at the end of the book. Note that there is a solution manual floating around for these selected problems.
A good comparison between Kay and Van Trees, and their complementary nature, can be established how they treat the description of the Cramer-Rao bound, Kay emphasizes the recent developments and derivations (mostly of arithmetic and bookkeeping nature, results from post 1968 papers), whereas Van Trees goes leaps and bounds and discusses other bounds which apply when Cramer-Rao does not. I appreciate having both books as a result.
It is interesting to note that after almost 20 years using Van Trees in a couple of courses, I can still navigate my way through the book with ease since it well organized and methodical.
RECOMMENDATION: BEST BUY.
But dont stop here, and buy Wozencraft/Jacobs and Gallager as well.
A Classic Text.......2002-05-23
I have just taken this course from Dr. Van Trees at GMU. Est&Det organized many concepts taught in other graduate engineering courses into a coherent philosophy. The result is not only a rich understanding of estimation and detection, but also random processes, Wiener filtering, Kalman filtering, radar and communications theory etc.
The course was taught directly from the text with little outside material. Very little has become obsolete in the 30+ years since it was written.
The strong positives of this book are the philosophical organization, clear concise writing, and incredibly well conceived homework problems.
The only negative of the book is that there are many proofs done in great detail. This provides the necessary foundation for the material, but also makes it easy for the student to lose track of the bigger picture.
Dr. Van Trees tends to try to drive home the higher level concepts while glossing over many of the details when he is lecturing. The exercise problems then force the student to give the necessary attention to pertinent details. In my opinion, this is an excellent approach to teaching the material.
Overall, this course was as good as any I've every taken. The text is as important and useful as any other I have.
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- Plastic Part Design for Injection Molding : An Introduction (Spe Books.)
- Practical Introduction to Data Structures and Algorithms, Java Edition
- Probability and Computing: Randomized Algorithms and Probabilistic Analysis
- Probability: The Science of Uncertainty with Applications to Investments, Insurance, and Engineering
- Programming the Microsoft Windows Driver Model
- Quantitative Business Modeling
Books Index
Books Home
Recommended Books
- Singled Out: How Singles are Stereotyped, Stigmatized, and Ignored, and Still Live Happily Ever Afte
- Insight Meditation: A Step-By-Step Course on How to Meditate
- Human Capital: A Novel
- History: Fiction or Science
- I've Seen Santa!
- Introduction to Space Physics
- National Security and The Nuclear Dilemma, 1945-1991
- The Life and Legend of E. H. Harriman
- Informe Sobre El Desarrollo Mundial 1993
- Grant and His Campaigns: A Military Biography
Nonlinear Regression Analysis and Its Applications (Wiley Series in Probability and Statistics)
Linear Regression Analysis (Wiley Series in Probability and Statistics)
Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting
Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics)
Bootstrap Methods and Their Application (Cambridge Series in Statistical and Probabilistic Mathematics , No 1)
