Book Description
Valuable software, realistic examples, and fascinating topics . . . everything you need to master the most widely used management science techniques using Microsoft® Excel™ is right here! Learning to make decisions in today's business world takes training and experience. Cliff Ragsdale--the respected innovator in the field of management science--is an outstanding guide to help you learn the skills you need, use Microsoft Excel for Windows to implement those skills, and gain the confidence to apply what you learn to real business situations. SPREADSHEET MODELING AND DECISION ANALYSIS gives you step-by-step instructions and annotated screen shots to make examples easy to follow. Plus, interesting sections called The World of Management Science show you how each topic has been applied in a real company.
Customer Reviews:
A Good Book for Finance/IT majors.......2007-09-17
This book does what it sets out to do: teach spreadsheet modeling. I'm only on the third chapter, but the author does a good job including step by step instructions on how to create winning models. The author is also very easy to understand. So if you're going to be doing optimization and modeling in your work, I highly recommend this book.
Great book, and includes @RISK.......2007-08-06
Ragsdale really makes spreadsheet modeling accessible to real-world business situations. It was a great asset to my MBA coursework. As a student, it came with a free student version of @RISK risk analysis software as well.
Good practical text.......2006-11-12
A good book for those studying decision making techniques or as a reference for managers looking to upgrade their skills
Decision analysis.......2006-11-02
Excellent book; I am considering it as a textbook for a Managerial Sciences course. The examples are clear and real increasing the interest of the students.
Good book , worth to read.......2006-02-17
This book is designated as the textbook for our master's level management modeling class. The author concerntrated on the application of Microsoft Solver to solve various of optimazation problems that we freqently faced in the real business opreations. Overall, this is good book for entry-level management modeling study.
Book Description
In recent years, there has been growing interest in the development and application of quantitative statistical methods to study choices made by individuals. This primer provides an introduction to the main techniques of choice analysis and also includes details on data collection and preparation, model estimation and interpretation and the design of choice experiments. A companion website offers practice data sets and software to apply modeling and data skills presented in the book.
Customer Reviews:
Everything you need to learn to carry out a choice model.......2005-10-17
This book is massive, and hence the term "primer" may be a little misleading. But if you really want to understand how to model choice data for a range of models, the book is outstanding. Other books focus more on the econometrics of the models, which are pivotal to know. But this book builds upon that by walking you through a series of increasingly-complex models, allowing you to understand why you need to perform particular modeling tasks.
The book focuses on NLogit software, but once you understand how to actually estimate choice models utilizing software, the skills can be easily carried into other software programs. However, without such experience, the other books available may fall short in enabling you to estimate choice models. Indeed, this was the case for me -- I understood the econometrics of the models, but had difficulty estimating complex models using software, simply beacuse I was uncomfortable with the syntax.
Hensher et al. removes this obstacle by giving the reader thorough training in both understanding what a given set of choice data may represent (e.g., observations from a particular choice experiment), and how to physically estimate models. The increase in confidence I received from working through the exercises in the book is why I rate the book so highly. Not only are the econometric concepts explained, but the nuts and bolts of model estimation are revealed, and that made all the difference.
An ambiguous oriented book.......2005-10-05
The whole book serves as a software (NLogit) manual. If you already know about the discrete choice analysis, you might be able to find out the messages that the authors try to convey. And it contains barely new information, so it doesn't help you anyway. But if you are new to this area, this is not the good book for you to start.
The book is extremely verbose and the ideas are hidden behind lines and ill-presented. It turned out that it's very difficult to comprehend the essence or even sense of the choice methods from this book. The best one can get is becoming a software user of the authors' own program.
Besides, the software, Nlogit, is not user-friendly and can't serve as a mainsteam tool.
Book Description
Master data analysis, modeling, and spreadsheet use with DATA ANALYSIS AND DECISION MAKING WITH MICROSOFT EXCEL! With a teach-by-example approach, student-friendly writing style, and complete Excel integration, this business statistics text provides you with the tools you need to succeed. Margin notes, boxed-in definitions and formulas in the text, enhanced explanations in the text itself, and stated objectives for the examples found throughout the text make studying easy. Problem sets and cases provide realistic examples that enable you to see the relevance of the material to your future as a business leader.
Customer Reviews:
Managerial Statistics Text book.......2006-11-03
It was the text book the professor wanted me to buy.
It was good.
Sanjay Chheda.......2006-10-06
The book is very good with really good explanations and examples on descriptive analysis and inferential analysis.
Better Title: Intro to Statistics using Excel Add-ins.......2001-06-04
On the positive side, this book has many excellent case studies and examples. It is well written and interesting. However, I was disappointed, as I was expecting use of Excel to rigorously solve decision making and data analysis problems. The focus of the book is mostly traditional statistics solved using a group of commercial add-ins for Excel. If this is what you want, then the book would get five stars. However, for data analysis and decision making, I think a more thorough treatment using Excel without relying so much on the add-ins would have been appropriate.
Serious Excel 2000 Problem.......2001-04-12
The text book is great. I have many of Winston's other books and they are all great. The Palisade stuff works just fine. However, the StatPro Addin that accompanies this text does not work with MS Excel 2000. I contacted the IT guy that the authors directed me to--he was stumped. He just gave up and suggested I return my book for a refund because he could not figure out it out. Again, the book is great but the StatPro Addin sucks!
No trouble with Excel.......2001-01-31
I find the text and software a useful set of tools. It assumes familiarity with basic statistics and Excel, and builds on them to develop a powerfull ability to analize data and make decisions from it. I experienced no trouble with the software install or operation.
Book Description
Thoroughly updated and more straightforward than ever, Applied Linear Regression Models includes the latest statistics, developments, and methods in multicategory logistic regression; expanded treatment of diagnostics for logistic regression; a more powerful Levene test; and more. Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.
Customer Reviews:
Must have reference.......2007-02-16
If you are going to spend money, buy the best. This book is the best and IS the standard. I'd consider this the "Gray's Anatomy" of Applied Linear Statiscal Models (i.e. Design of Experiments, Regression, hypothesis testing).
This book is geared for an entry level masters or 400 level student. If you don't fall into this category, this could be worthwhile, just know you'll need to put more time in to learn the material...or...you could get a book geared toward your level. Vardeman's applied statistics for engineers would be one that comes to mind for subject matter that is geared for knowledge below KNNW's Applied Linear Statistical Models.
Bottom line is that this is a must have in anyone's library who is going to do statistical analysis using linear models. It's one of my (and most of my co-workers) go to books if we need to refresh on a quick method to approach a problem.
All in all, it covers all the basics and for the money is a great applied book.
Cheaper Versions Available.......2007-02-12
This hard-bound text was received in excellent condition and should last for as long as I plan on using it; however, there are cheaper versions (like the international version) that contain exactly the same information (plus additional information about ANOVA designs). I am still happy with my purchase, but if you are low on cash, I would recommend purchasing a different edition of this book.
the author don't know how to express in simple language.......2007-01-16
the author don't know how to express in simple and understandable language, although he know very well in this major. I have already read some other books of this major, it is still confusing me a lot to understand some sentences in this book.
Super ! .......2007-01-09
This book if not for business majors , engineering students and psycology students.
This is an EXCELLENT book for statistics undergrad/grad and PhD students.
I spent over 10 hours weekly just reading the book every week. Plus my assignments will take another 10 hours . So be prepared for a 20 hr week.
YOU NEED TO TAKE A BASIC STAT / INTRO STAT course before this. If you dont know the meaning of P-values , T-test , F-test , DO NOT TAKE THIS COURSE. This book will not introduce you to those things. Unfortunately many buiness schools ( including top 10 ) dont offer a good intro stat course, so buiness majors jumping in to this course is a wrong idea.
This book is also a "good to own book". The first 15 or so chapters has regression and the second half ( next 15 chapters ) has DOE (design of experiments). GREAT BOOK !
One piece of advice - make sure you learn to use SAS with this course . In real world applications many industries are using SAS. Even if your teacher insists on using R package / splus , YOU MAKE SURE YOU know how to do those things in SAS . There is a SAS student manual with this book, specially written for this book . buy it ISBN - 0-07-302177-6
good luck !
Popularly accepted regression text book.......2006-11-06
I bought this book because I needed it for a class, and I have only used it a few times for the class. It's hard to learn stats from a textbook unless you start at the beginning, but this book is useful to accompany a previously-knowledgeable statistics mind seeking to learn more about regression.
Great book, but probably will not help a rookie to self-teach regression.
Average customer rating:
- A classic that is very relevant today
- Checkland's masterpiece
- Really worthwhile
- Where it all began...
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Systems Thinking, Systems Practice: Includes a 30-Year Retrospective
Peter Checkland
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ASIN: 0471986062 |
Book Description
Systems Thinking, Systems Practice "Whether by design, accident or merely synchronicity, Checkland appears to have developed a habit of writing seminal publications near the start of each decade which establish the basis and framework for systems methodology research for that decade." Hamish Rennie, Journal of the Operational Research Society, 1992 Thirty years ago Peter Checkland set out to test whether the Systems Engineering (SE) approach, highly successful in technical problems, could be used by managers coping with the unfolding complexities of organizational life. The straightforward transfer of SE to the broader situations of management was not possible, but by insisting on a combination of systems thinking strongly linked to real-world practice Checkland and his collaborators developed an alternative approach - Soft Systems Methodology (SSM) - which enables managers of all kinds and at any level to deal with the subtleties and confusions of the situations they face. This work established the now accepted distinction between 'hard' systems thinking, in which parts of the world are taken to be 'systems' which can be 'engineered', and 'soft' systems thinking in which the focus is on making sure the process of inquiry into real-world complexity is itself a system for learning. Systems Thinking, Systems Practice (1981) and Soft Systems Methodology in Action (1990) together with an earlier paper Towards a Systems-based Methodology for Real-World Problem Solving (1972) have long been recognized as classics in the field. Now Peter Checkland has looked back over the three decades of SSM development, brought the account of it up to date, and reflected on the whole evolutionary process which has produced a mature SSM. SSM: A 30-Year Retrospective, here included with Systems Thinking, Systems Practice closes a chapter on what is undoubtedly the most significant single research programme on the use of systems ideas in problem solving. Now retired from full-time university work, Peter Checkland continues his research as a Leverhulme Emeritus Fellow.
Customer Reviews:
A classic that is very relevant today.......2007-06-13
I originallay read (and wrote a paper about) Checkland's ideas in 1990 whilst I was studying for my MBA. Then his ideas seemed revolutionary, insightful and impractical. Re-visiting his book nearly 20 years on little has changed in my view of its content, but the world has moved on and what seemed impractical now appears possible.
I would urge anyone involved in creating modern systems based on distributed and dynamic principles to study Checkland.
Checkland's masterpiece.......2004-10-23
When I first read this book I thought it to be revolutionary, ahead of it's time (as others have) and insightful. Despite the fact that Checkland has in large moved away from the ideas and the model of this book - to me it represents the original vision of SSM (soft systems methodology) more so than his later books. Checkland presents a history of systems thinking in the book then goes onto to discuss the need for a new approach - that of SSM. With extreme elegance of style Checkland delivers a long and stinging critique to Hard Systems thinking and presents a coherent and thoughtful argument for his own version SSM. Further he creates a platform for real world problem solving that is useful and interesting. A lot of his ideas have appeared in American texts (like the fifth discipline for example) and rarely are they credited or made use of in that regard. This book is a good place to start exploring the real world of problems with but I would highly recommended that before you go to his two other books you start here. This in my opinion has not been bettered in any systems context to date and I am not sure it ever will or could be. Having said that you really do need to read it and find out for yourself. Be warned it's not for those who want to be challenged in their thinking - especially those of you who don't like the qualitative stuff.
Really worthwhile.......2004-07-02
This book is a gem. The basic concepts of systems, hierarchies and emergent properties are developed from the methodologies of physical and social sciences in chapter 3, and makes for fascinating reading. I'm currently writing a master's thesis on it! =)
If you're studying management of information systems or something similar, you are probably sick and tired of overly theoretical approaches to the subject which seem to be just excuses for academics to publish rubbish (eg. structuration, actor network theory, etc). This book may save you from a nervous breakdown.
Where it all began..........2002-07-12
Well, since I've been on a bit of a 'systems' binge lately, I might as well review this old gem...
Checkland's book was the first to introduce the differentiation between 'soft' and 'hard' systems analysis. Soft analysis is much more akin to a general, somewhat philosophical approach to the methodology whereas hard analysis is the development of usable engineering models.
First off, this book is actually two books - the first is a fairly long paper that neatly sums up the systems approach over the 30 years it has been explored. The consensus? Things looked really promising at the beginning but unfortunately the approach simply got hung up on the very thing it was trying to escape: science's current preoccupation with reductionism. That is, the hard systems approach attracted the most attention and it quickly succumbed to the very trap it sought to escape starting with its use of rigidly-defined symbols right up to the detailed diddling with mathematical models that, similar to earlier approaches, did not model reality at all due to assumptions and oversimplification.
Checkland is much more interested in the soft approach and he consistently laments the fact that systems methodology is not being taught even though it holds so much promise to solving many of our pressing problems. The overview presses this point home and should be required reading for anyone in management or engineering.
The second section, the original book with a few revisions, is still very relevant. Checkland's focus, soft systems, never was given a chance given our preoccupation with reductionism. Given the recent failures of reductionism, particularly the genome-mapping fiasco, cast systems theory in new light.
Checkland starts out with an excellent overview of the history of science from a (mostly) philosophical perspective. This very readable overview leads directly into his discussion of the history and early development of systems theory. He then focuses on systems methodology (soft systems theory) with some general applications.
The approach is very readable and should be easily understood by anyone - in fact, Checkland stresses the importance of having a wide base of knowledge to help solve real-world problems and points out that much work has been done by people who 'migrated' from other fields. Smuts, one of the pioneers, was actually a politician and only wrote a systems book after losing an election...
It is unfortunate that there are no references to Robert Rosen here since his work, more of a 'hard' approach to systems theory, fully supports Checkland's ideas. In fact, there is a lot of material that should be included as 'backup' for why the systems approach is important as a new direction away from reductionism. Perlovsky's work in cybernetics, Jopling's recent work on self-knowledge, Prigogine's work in thermodynamics and even Kauffman's attempts in biology now point to hypotheses that are only compatible with a systems methodology.
This book, as mentioned above, should be required reading these days. Certainly for anyone contemplating management or engineering it is a very important reference. In fact, the book could basically be used in high-school with a bit of help from Weinberg's systems books. For those looking for more application-specific information I recommend von Bertalanffy's original, Rosen's work, and perhaps a side helping of Weinberg and Gharajedaghi for more ideas.
Average customer rating:
- Elegant and astonishing
- A Good Book but Seriously Overpriced
- This is a true classic
- Simply the perfect math book
- Thank You Dr. Luenberger
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Optimization by Vector Space Methods (Series in Decision and Control)
David G. Luenberger
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ASIN: 047118117X |
Book Description
Engineers must make decisions regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically analyzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems. Unifies the large field of optimization with a few geometric principles. Covers functional analysis with a minimum of mathematics. Contains problems that relate to the applications in the book.
Customer Reviews:
Elegant and astonishing.......2006-03-17
Professor Luenberger unites many areas of optimization using a few principles from functional analysis. The explanations are clear and the proofs are compact and elegant. This book is your tool for understanding the deep connection between linear programming, convex optimization, game theory, optimal control and series approximation (e.g. Fourier series).
Luenberger's book has over 1300 citations as of March 2006. In my opinion, the material in this book is essential for any graduate student or professional who intends to contribute to the literature in optimization or optimal control.
A Good Book but Seriously Overpriced.......2005-01-15
The exposition is pretty clear and the book has a good number of worked non-trivial examples. At $40 this would be a great book, but $100 for a PAPERBACK book written 30 years ago is a bit ridiculous. The first 1/4 of the book is also a (very) basic introduction to functional analysis which, if you have had any contact with this subject before, you will probably skip making the book quite short.
This is a true classic.......2004-12-18
This book is a timeless classic, filled with extraordinarily powerful mathematics and applicable to just about every serious subject area. Luenberger did a masterful job of writing a book that will "unravel the spaghetti" seen in most other books. The visual perspectives he provides to seemingly abstract ideas are the key.
Simply the perfect math book.......2003-07-04
Optimization by Vector Space Methods, by David Luenberger, is one of the finest math texts I have ever read, and I've read hundreds. Many years ago this book sparked my interest in optimization and convinced me that the abstract mathematics I had been immersed in actually would be applicable to real problems. Since then, Luenberger's book has inspired several of my graduate students. I merely lent them my copy, and Luenberger did the rest; he drew them in by carefully laying the foundation for an elegant theory, with just the right mix of formalism and intuition, and opened their eyes to the beauty and practicality of abstract mathematics. Anyone with an interest in higher-level mathematics (beyond multi-variable calculus, say) would benefit from exposure to this finely-crafted book. I daresay, the rampant math anxiety that is so prevalent in the West would be substantially reduced if more authors would take such meticulous care in presenting their material.
The format of Luenberger's book is also extremely appealing in a way that I cannot quite put my finger on. The typography and illustrations are inherently crisp and inviting; they draw you in. There is nothing at all superfluous or gratuitous in this book. It is utterly to-the-point, methodical, and above all, clear. The techniques are developed starting from an elementary treatment of vector spaces, then proceeding on to Banach spaces and Hilbert spaces. Along the way, Luenberger introduces convexity, cones, basic topology, random variables, minimum-variance estimators, and least squares, among many other things. There is a recurring theme of duality, which can be used in a way analogous to the inner product of a Hilbert space. In particular, the familiar projection theorems of Hilbert spaces can be echoed in simpler normed linear spaces using duality, which Luenberger motivates and covers beautifully.
The book also covers some of the standard fare of functional analysis, such as the Han-Banach theorem, strong and weak convergence, and the Banach inverse theorem. However, Luenberger never wanders too far off into abstract nonsense; around every corner lay tantalizing application of these ideas to optimization. Luenberger first explores optimization of functionals then covers constrained optimization, which builds upon concepts such as positive cones and Lagrange multipliers. The optimization methods themselves have endless applications in fields such as computer vision, computer graphics, economics, and physics. Indeed, the list is effectively endless as optimization techniques pervade math and science.
I'm certain that the appeal of this book is helped immeasurably by the inherent beauty of the subject matter. Hilbert-space methods are lovely in themselves--they possess a structure that engages one's geometric intuition while at the same time admitting convenient algebraic properties. Once you are in the habit of phrasing problems in abstract settings such as Hilbert spaces, it forever changes how you look at things; you cannot help but look past the clutter to the essence of a problem (or, at least try very hard to do so). While this material is not nearly as abstract as, say, category theory, it nevertheless hits a high point in mathematics--a point more people ought to experience.
If you've had some exposure to optimization methods, or need to apply them in the context of computer vision, graphics, or finance, to mention just a few areas, then I urge you to take a look at Luenberger's fine book. It too hits a high point in clarity of mathematical writing. Combine beautiful theory with endless applications and lucid writing, and you have a winner of a book.
Thank You Dr. Luenberger.......2002-10-15
I owe Dr Luenberger a million thanks for writing this book. As his student, I think he is the master of putting complex issues in simple words. Your faithful student..Jayanth Krishnan
Book Description
In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners. With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.
Customer Reviews:
More like a handbook.......2007-09-07
We used this book for our intro to Bayesian statistics class at SDSU. I thought it was more like a technical manual for how to do Bayesian statistics, rather than a good introductory textbook. Recommended for researchers who want to know the nitty-gritty of MCMC and the like. Not a good textbook for a first course in Bayesian statistics. To understand what was going on in class I used Lancaster, "An Introduction to Bayesian Econometrics". Much better intuitive explanation of what is going on.
An good overview of the corps of the matter.......2001-11-20
This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.
Book Description
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
- Comprehensive coverage of an imporant area for both research and applications.
- Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
- Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
- Includes a number of applications from the social and health sciences.
- Edited and authored by highly respected researchers in the area.
Customer Reviews:
Infer causality!.......2005-04-14
I would recommend this book to any social scientist who is interested in learning statistical methods that can allow them to say that one thing causes another --- and who doesn't? The classical methods taught to social scientists do not allow you to infer causality, and these methods are actually conceptually a lot easier than the classical methods --- at least, I have a much easier time explaining them to non-statisticians than most regression methods. And now they are not even so hard to implement, especially with new software like Jas Sekhon's match library for R.
I also recommend this book to anyone who is working with these methods already. It is helpful to see others' completed projects on similar topics, and it is impressive to see the breadth of topics that the Rubin Causal Model has been applied to. As statistics books go, it is surprisingly human since they emphasize how many of the authors all belong to this one big Statistical Family, as they call it, and they even have a family tree; also, the introduction including the other titles that they considered for the book made me laugh out loud.
If you do decide to buy the book, I recommend that you froogle it first, since its price distribution is left-skewed with some surprisingly low outliers.
Book Description
This important book provides information necessary for those dealing with stochastic calculus and pricing in the models of financial markets operating under uncertainty; introduces the reader to the main concepts, notions and results of stochastic financial mathematics; and develops applications of these results to various kinds of calculations required in financial engineering. It also answers the requests of teachers of financial mathematics and engineering by making a bias towards probabilistic and statistical ideas and the methods of stochastic calculus in the analysis of market risks.
Customer Reviews:
Shiryaev knows his stuff!.......2006-09-13
This book is typical of Shiryaev, who is a representative of the Russian school of probability theory. Not only the book explains the technical details clearly, but also it explains the "bigger picture" as to why this particular mathematical set-up makes sense and it is a good approximation of reality. The book reflects the (admirable) Russian style of teaching: explain the origins of theory, which are usually some specific problem; then carefully develop a mathematical theory tailor-made for the given problem; finally, disclose the essence of the problem and produce a beautiful result.
Before reading this book, I have been quite familiar with stochastic calculus and semi-martingale theory. What interests me most (sometimes puzzles me most) is the way how theory is applied to financial math problems, esp. the justification of certain "conventions" (e.g. we always start with discounted process, play with martingale measures, and do certain standard "rituals" in pricing and hedging). Sometimes people abuse those conventions when the theory's set-up is not quite appropriate. Shiryaev's book shows the justification and limitation of theory, by clearly explaining the origins and specific contexts of theory. This is especially helpful to getting a true understanding of the subject. I would say after reading his book, my mind has achieved a harmony.
I read Shiryaev's book on probability (GTM 95) many years ago. It was a pleasant experience. Now I'm happy to have this kind of experience again. From lines of the book, you can see the author's passion and deep understanding of financial math.
I only regret I didn't read this book much earlier.
Excellent Monograph.......2005-07-21
This monograph starts from the very basics and develops as it progresses. Its historical notes found all over the book makes it unique and entertaining. As a mathematician aspiring to break through the STREET, I found it very accessible and comprehensive. If you have Brownian Motion and Stochastic Calculus at Shever/Kaaze's (how ever you spell their name) level, you will skim through this book with in weeks. But if you don't, don't panic, you will still be fine with some introductory level measure theoretic probability course.
You will enjoy it as I did.
Bravo.......2001-11-14
The Essentials of Stochastic Finance: Facts, Models, Theory by Albert N. Shiriaev, et al offers a clear treatment of both theoretical and emperical Finance. Shiryaev presents not only the essentials of probability as it is applied to finance,but he also covers recent develpoments in Mathematical Finance. It is very well written and it can be covered in one year (depending on the audience). Each topic moves from the specific to the general, beginning with one or more examples to lead into the theoretical results. This is the most comprehensive book out there. It covers Mathematical Finance, Martingale, Markov Thoery... to Econometric ARCH GARCH FGARCH ...to theory of Finance CAPM APT... PART II of the book requires a good knowledge of Stochastic Calculus at Karatzas and Shreve level...
Outstanding...
Book Description
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book
· Provides a broad and comprehensive account of applied Bayesian modelling.
· Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.
· Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.
· Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.
The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.
Download Description
"The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book · Provides a broad and comprehensive account of applied Bayesian modelling. · Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications. · Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology. · Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site. The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis."
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