It is one of my best book that I have had for all my life. It has a clear english and also I can make good comments on the examples given in the book.

~This is a comprehensive and self-contained book for serious readers with certain mathematical maturity, or at least with persistence and patience, as well as the willingness to go "deep" into the whole theory.

In my opinion, Semimartingale Theory and Stochasitc Calculus is a textbook version of the "bible" on stochastic calculus, Probabilities and Potential A and B, by Dellacherie and Meyer.
Unfortuanately, the latter is kind of unreadable, containging some mistakes/typos and assuming too much on the readers, esp. volume 1. Compared with this unreadable "bible", He-Wang-Yan's book has the following merits:
1. It contains the essence of Dellacherie/Meyer, but debugs/simplifies many proofs.
2. It gives a concise and clear presentation of Dellacherie's capacity theory, which is essential in recognizing the measurability of sets in product space and when a positive r.v. becomes a stopping time.
3. It presents carefuly the formidable general theory of stochastic processes, which is hard to find in English books, except in Dellacherie/Meyer.
4. This book is supplemented and further developed in the form of problems. Some of the problems are useful results and some of them are difficult.
5. Finally, this book is not limited to Dellacherie/Meyer. The last six chapters also report on some recent development of the theory, up to the time the book was published. In particular, the last two chapters can be seen as a short presentation of some materials contained the classic book, Limit Theorems for Stochastic Processes, by Jacod/Shiryaev.

Although this book is little known in US, European mathematicians
think highly of it. In a restrospection book on stochastic calculus, LNM 1771, Séminaire de Probabilités 1967-1980--A Selection in Martingale Theory, Yor and Emery commented: "...To the best of our knowledge, only two books in that language (English) provide a self-contained account of stochastic calculus, with a complete proof of the optional and previsible
section theorem: Dellacherie-Meyer, Probabilities and Potential A and B, North-Holland 1978 and 1982; He-Wang-Yan, Semimartingale Theory and Stochastic Calculus, CRC Press 1992."

However, I want to warn future readers that the approach of this book is "traditional". So you may have to go a long way to see the definition of stochastic integration. In this regard, I would recommend Protter's book, Stochastic Integration and Differential Equations: A New Approach. But be cautious, Protter's book is not easier. It'd be really beneficial looking at these two books simutaneously--in Protter's book, you'll see some of the deepest results of the theory, treated so neatly that you'll really be grateful.

This book and the journal articles on which it is based pioneer a new branch of statistics. The reader who is unfamiliar with semimartingales can think of them as a generalization of supermartingales, where the latter are roughly a sequence of variables whose means increase and such that each variable is bigger than its conditional mean (a conditional mean is the mean of one thing or variable with another thing or variable fixed). Intuitively, when you use semimartingales to approximate or model something in the real world, you are approximating its mean by a sequence of increasing or decreasing means or alternately both, keeping track of where and when the means increase or decrease inside the mathematics. This allows you to make fewer assumptions than usual about how the things that you are modeling behave, since roughly you only have to keep track of the means. This has an advantage over computer techniques now in common use such as linear and polynomial regression. This book shows that you can actually make statistical estimates for the unknown quantities in your model for large samples ("asymptotically"). The reader is cautioned that a somewhat parallel but interesting theory exists with conditional means replaced by logic-based probability (LBP) means, which is to say that division is replaced by subtraction and adding 1 to the result. The latter allows study of very rare events since it is defined when events have probability zero, and also other events of importance. See some of my reviews of other mathematics books or my articles abstracted on the internet at the Institute for Logic of the University of Vienna for LBP methods.

This book has an excellent command on semiparametric methods. These methods are frequently used in econometrics. As a tradition many economists used to estimate parametric models. But estimating parametric models requires many assumptions. First of all while estimating a parametric model you assume that you know the functional form of data generating process. If your assumption is not true, your model will not give estimates for the conditional mean function. Thus, economists began to use nonparametric models. However, in order to use nonparametric methods, we need a very large data set, which is not the case in many economic applications. Thus semiparametrioc methods are efficient to implement in many applications. Joel Horowitz is one of the most experienced people in this area and his book is a good point to start learning semiparametric methods.

Too often modellers do not realise that sensitivity analysis is an essential part of the model building process. This volume has a didactical value showing how SA is often useful - and sometimes essential- to complete the model building process and to interpret results properly. It guides the reader through an array of different approaches, illustrating in a generally clear fashion the specificity of the different techniques to different problem-setting.

Although this is a multi-authored book, the discourse flows clearly across (most of) the chapters and coveys the main element of this new discipline.

The authors-editors show an overall preference for sensitivity analysis methods capable of global quantitative sensitivity analysis; the sections of the book devoted to local methods and to regression analysis are rather a useful review than actually new material. The sections on variance-based methods and on high dimensional model representations are probably the most instructive for the educated reader.

The applications are in general well presented and instructive. These range from atmospheric chemistry to material physics. A chapter on available software is also offered. Finally the chapter from Beck and Chen (Assuring The Quality Of Models Designed For Predictive Tasks) establishes the needed link between the present raging debate on model validation and the use of adequate sensitivity analysis methods.

Many excellent books analyze models/methods to represent curves and surfaces in Computer-Aided Design &
Manufacturing (CAD/CAM) systems, but this is the first one focusing on solving some very complex geometric
modeling problems underlying many engineering applications. The authors identify these bottleneck problems,
analyze them in detail, and present state-of-the-art solution algorithms. These important problems are:

"Intersection problems" (Chapters 5 and 6), "Distance calculations" (Chapter 7),
"Curve/Surface Interrogation" (Chapters 8 and 9), "Computation of Shortest Paths" (Chapter 10), and
"Offset Curves and Surfaces" (Chapter 11).

In each one of the above chapters, the corresponding problem is fully analyzed using tools from advanced
Geometric Modeling, Numerical Methods and Differential Geometry, some of which are new results previously
available only in research journals. The authors have done an excellent job in collecting all these results in
a single volume, offering an invaluable tool to CAD/CAM professionals who do not have the time to study
and analyze themselves all this literature.

The solutions proposed go beyond standard tricks and heuristics: the problems are formulated as systems
of nonlinear equations and reliable solution-algorithms are offered based either on standard methods or
on fresh research. Robustness issues (accuracy, treatment of special cases, singularities, etc) are at the center
of discussion guiding the reader on dealing with the most difficult aspects of geometric-software development.

In short, this book is a powerful tool for CAD/CAM professionals (software architects/developers,
consultants, engineers/users, academic teachers and researchers, etc) as:
1) Very useful results are analyzed, that alternatively are available only in advanced mathematical texts and current research publications.
2) Problems are discussed in detail using theoretical results as well as examples.
3) Robust solution methods are presented, based on a solid mathematical analysis as opposed to heuristics.
4) All prerequisites are fully analyzed in the first 4 chapters, making the volume self-contained.

amazing is all i can call it... as a marketing managememt student i found myself having to study complicated book about all the things i need to know... specially when it come to studying statistical analysis... but this book can really explain in a very funny, interesting and pratical maner, allowing us to study like if we were reading a cartoon's book... I wish some of my teachers could do the same as this book... ;)

R.H.J.M Otten and L.P.P.P van Ginneken The Annealing Algorithm Kluwer Academic Publisher

This is an excelent textbook explaining what simulation means and how to deepen your knowalge. You have to be a good programmer in order to use this book (and simulation generally)and the author should have added an andex for such a language and how its connection with simualtion ( C or C++) although my experience would elect MATLAB as prefernce!! This text does not requir any prior experience regarding simulatin although taking a course in statistics and probability would be advantageous!!

As with every book he wrote, the late Professor Miller gives a thorough and scholarly treatment to a difficult topic. This 1981 edition is the second edition of this book originally published by a different publisher in 1966. Both editions have been heavily cited in the statistical literature. This gives you everything you every wanted to know about simultaneous confidence intervals and hypotheses test but were afraid to ask. It is a great reference book.