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<p>Statistical Distributions (3 edition) by Merran Evans , Nicholas Hastings , Brian Peacock<br/>这是一本很好的参考书. 包括了基本的统计分布函数.<br/><br/>得益于这个论坛很久了,也想做一点小贡献 <img title="人大经济论坛 http://www.pinggu.org" alt="图片点击可在新窗口打开查看" src="http://www.pinggu.name/bbs/Skins/Default/emot/em01.gif" align="middle" border="0"/>  <br/></p><p> 303540.pdf (9.04 MB)

2009-3-14 04:54:00 上传

[分享]免费 Statistical Distributions by Evans

<br/><br/>【书名】 Statistical Distributions <br/>【作者】Merran Evans , Nicholas Hastings , Brian Peacock <br/>【出版社】Wiley-Interscience<br/>【版本】3 edition<br/>【出版日期】June 15, 2000<br/>【文件格式】PDF<br/>【文件大小】9.3M<br/>【页数】221 pages<br/>【ISBN出版号】<b>ISBN-10:</b> 0471371246 <b>ISBN-13:</b> 978-0471371243<br/>【资料类别】统计学 <br/>【市面定价】$76.45 (美元)<br/>【扫描版还是影印版】清晰扫描版<br/>【是否缺页】是<br/>【关键词】power function variate, variate corresponds, rectangular variate, Parameter Estimator Method, Biometrika Trustees<br/>【内容简介】* Presents the 40 distributions in alphabetical order<br/>* Provides all key formulas for each distribution<br/>* Adds a new chapter on the Empirical Distribution Function<br/>* Expands the Weibull Distribution to cover the 3 and 5 parameter versions<br/>* Incorporates diagrams and tables illustrating the characteristics of each distribution<br/>* Discusses the types of application for which distributions are used<br/>* Features references to relevant software packages<br/><br/>【目录】Contents<br/>Preface<br/>1. Introduction<br/>2. Terms and Symbols<br/>2.1 Probability, Random Variable, Variate, and<br/>Random Number, 3<br/>2.2 Range, Quantile, Probability Statements and<br/>Domain, and Distribution Function, 5<br/>2.3 Inverse Distribution and Survival Function, 8<br/>2.4 Probability Density Function and Probability<br/>Function, 9<br/>2.5 Other Associated Functions and Quantities, 11<br/>3. General Variate Relationships 17<br/>3.1 Introduction, 17<br/>3.2 Function of a Variate, 17<br/>3.3 One-to-one Transformations and Inverses, 18<br/>3.4 Variate Relationships Under One-to-one<br/>Transformation, 20<br/>3.5 Parameters, Variate, and Function Notation, 22<br/>3.6 Transformation of Location and Scale, 24<br/>3.7 Transformation from the Rectangular Variate, 25<br/>3.8 Many-to-One Transformations, 26<br/>3.9 Functions of Several Variates, 29<br/>4. Bernoulli Distribution<br/>4.1 Random Number Generation, 32<br/>4.2 Curtailed Bernoulli Trial Sequences, 32<br/>4.3 Urn Sampling Scheme, 33<br/>4.4 Note, 33<br/>5. Beta Distribution<br/>5.1 Notes on Beta and Gamma Functions, 35<br/>5.2 Variate Relationships, 37<br/>5.3 Parameter Estimation, 40<br/>5.4 Random Number Generation, 41<br/>5.5 Inverted Beta Distribution, 41<br/>5.6 Noncentral Beta Distribution, 42<br/>5.7 Beta Binomial Distribution, 42<br/>6. Binomial Distribution<br/>6.1 Variate Relationships, 44<br/>6.2 Parameter Estimation, 46<br/>6.3 Random Number Generation, 47<br/>7. Cauchy Distribution<br/>7.1 Note, 49<br/>7.2 Variate Relationships, 49<br/>7.3 Random Number Generation, 50<br/>7.4 Generalized Form, 50<br/>8. Chi-Squared Distribution<br/>8.1 Variate Relationships, 53<br/>8.2 Random Number Generation, 57<br/>8.3 Chi Distribution, 57<br/>9. Chi-Squared (Noncentral) Distribution<br/>9.1 Variate Relationships, 59<br/>10. Dirichlet Distribution<br/>10.1 Variate Relationships, 62<br/>10.2 Dirichlet Multinomial Distribution, 63<br/>11. Empirical Distribution Function<br/>11.1 Estimation from Uncensored Data, 65<br/>11.2 Estimation from Censored Data, 66<br/>11.3 Parameter Estimation, 67<br/>11.4 Example, 67<br/>11.5 Graphical Method for the Modified Order-<br/>Numbers, 69<br/>11.6 Model Accuracy, 70<br/>12. Erlang Distribution<br/>12.1 Variate Relationships, 72<br/>12.2 Parameter Estimation, 73<br/>12.3 Random Number Generation, 73<br/>13. Error Distribution<br/>13.1 Note, 75<br/>13.2 Variate Relationships, 76<br/>14. Exponential Distribution<br/>14.1 Note, 79<br/>14.2 Variate Relationships, 80<br/>14.3 Parameter Estimation, 81<br/>14.4 Random Number Generation, 81<br/>15. Exponential Family<br/>15.1 Members of the Exponential Family, 82<br/>15.2 Univariate One-Parameter Exponential<br/>Family, 82<br/>15.3 Estimation, 84<br/>16. Extreme Value (Gumbel) Distribution<br/>16.1 Note, 86<br/>16.2 Variate Relationships, 86<br/>16.3 Parameter Estimation, 89<br/>16.4 Random Number Generation, 89<br/>17. F (Variance Ratio) or Fisher-Snedecor Distribution<br/>17.1 Variate Relationships, 91<br/>18. F (Noncentral) Distribution<br/>18.1 Variate Relationships, 96<br/>19. Gamma Distribution<br/>19.1 Variate Relationships, 99<br/>19.2 Parameter Estimation, 101<br/>19.3 Random Number Generation, 102<br/>19.4 Inverted Gamma Distribution, 103<br/>19.5 Normal Gamma Distribution, 103<br/>19.6 Generalized Gamma Distribution, 104<br/>20. Geometric Distribution<br/>20.1 Notes, 107<br/>20.2 Variate Relationships, 108<br/>20.3 Random Number Generation, 108<br/>21. Hypergeometric Distribution<br/>21.1 Note, 111<br/>21.2 Variate Relationships, 11 1<br/>21.3 Parameter Estimation, 112<br/>21.4 Random Number Generation, 112<br/>21.5 Negative Hypergeometric Distribution, 112<br/>21.6 Generalized Hypergeometric (Series)<br/>Distribution, 113<br/>22. Inverse Gaussian (Wald) Distribution<br/>22.1 Variate Relationships, 115<br/>22.2 Parameter Estimation, 116<br/>23. Laplace Distribution<br/>23.1 Variate Relationships, 118<br/>23.2 Parameter Estimation, 120<br/>23.3 Random Number Generation, 120<br/>24. Logarithmic Series Distribution<br/>24.1 Variate Relationships, 122<br/>24.2 Parameter Estimation, 122<br/>25. Logistic Distribution<br/>25.1 Notes, 126<br/>25.2 Variate Relationships, 127<br/>25.3 Parameter Estimation, 128<br/>25.4 Random Number Generation, 128<br/>26. Lognormal Distribution<br/>26.1 Variate Relationships, 130<br/>26.2 Parameter Estimation, 133<br/>26.3 Random Number Generation, 133<br/>27. Multinomial Distribution<br/>27.1 Variate Relationships, 136<br/>27.2 Parameter Estimation, 136<br/>28. Multivariate Normal (Multinormal) Distribution<br/>28.1 Variate Relationships, 138<br/>28.2 Parameter Estimation, 139<br/>xii<br/>29. Negative Binomial Distribution<br/>29.1 Note, 142<br/>29.2 Variate Relationships, 142<br/>29.3 Parameter Estimation, 144<br/>29.4 Random Number Generation, 144<br/>30. Normal (Gaussian) Distribution<br/>30.1 Variate Relationships, 146<br/>30.2 Parameter Estimation, 150<br/>30.3 Random Number Generation, 150<br/>31. Pareto Distribution<br/>31.1 Note, 153<br/>31.2 Variate Relationships, 153<br/>31.3 Parameter Estimation, 154<br/>31.4 Random Number Generation, 154<br/>32. Poisson Distribution<br/>32.1 Note, 157<br/>32.2 Variate Relationships, 157<br/>32.3 Parameter Estimation, 160<br/>32.4 Random Number Generation, 160<br/>33. Power Function Distribution<br/>33.1 Variate Relationships, 162<br/>33.2 Parameter Estimation, 164<br/>33.3 Random Number Generation, 164<br/>34. Power Series (Discrete) Distribution<br/>34.1 Note, 166<br/>34.2 Variate Relationships, 166<br/>33.3 Parameter Estimation, 166<br/>35. Rayleigh Distribution<br/>35.1 Variate Relationships, 168<br/>35.2 Parameter Estimation, 169<br/>36. Rectangular (Uniform) Continuous Distribution<br/>36.1 Variate Relationships, 171<br/>36.2 Parameter Estimation, 174<br/>36.3 Random Number Generation, 174<br/>37. Rectangular (Uniform) Discrete Distribution<br/>37.1 General Form, 176<br/>37.2 Parameter Estimation, 177<br/>38. Student's t Distribution<br/>38.1 Variate Relationships, 180<br/>38.2 Random Number Generation, 183<br/>39. Student's t (Noncentral) Distribution<br/>39.1 Variate Relationships, 186<br/>40. Triangular Distribution<br/>40.1 Variate Relationships, 188<br/>40.2 Random Number Generation, 188<br/>41. von Mises Distribution<br/>41.1 Note, 190<br/>41.2 Variate Relationships, 191<br/>41.3 Parameter Estimation, 191<br/>42. Weibull Distribution<br/>42.1 Note, 193<br/>42.2 Variate Relationships, 194<br/>42.3 Parameter Estimation, 194<br/>42.4 Random Number Generation, 196<br/>42.5 Three-Parameter Weibull Distribution, 196<br/>42.6 Three-Parameter Weibull Random Number<br/>Generation, 197<br/>42.7 Bi-Weibull Distribution. 197<br/>xiii<br/>170<br/>xiv<br/>42.8 Five-Parameter Bi-Weibull Distribution, 199<br/>42.9 Weibull Family, 202<br/>43. Wishart (Central) Distribution<br/>43.1 Note, 205<br/>43.2 Variate Relationships, 205<br/>INDEX<br/>44. Computing References 206<br/>45. Statistical Tables 210<br/>45.1 Normal Distribution Function, 211<br/>45.2 Percentiles of the Chi-Squared Distribution, 212<br/>45.3 Percentiles of the F Distribution, 214<br/>45.4 Percentiles of the Student's t Distribution, 218<br/>45.5 Partial Expectations for the Standard Normal<br/>Distribution, 219<br/>Bibliography 220<br/>【书评】整理书评】Provides a concise summary of facts and formulas relating to 40 major<br/>probability distributions, together with associated diagrams that allow<br/>the shape and other general properties of each distribution to be<br/>readily appreciated. Introductory chapters cover fundamental concepts<br/>and describe rules governing relationships between variates. Extensive<br/>use is made of the inverse distribution function. This third edition<br/>includes more distributions and new material on applications, variate<br/>relationships, estimation, and computing. Evans is Director of Planning<br/>and Academic Affairs at Monash University, Australia.</p><br/>

[此贴子已经被作者于2009-3-14 5:10:25编辑过]

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关键词：distribution Statistical statistica statistic Statist 分布函数 出版社 统计学 资料

楼主 这真的是免费的吗……

sqy:

我发给论坛管理者的时候并没有提到要金币,是他们好意加的金币额度,我会发邮件请他们更正.但是我不欣赏你这种说话的口气.

----------------------------------

@逆水行舟：已删除sqy的帖子。

                  -------------wesker1999

[此贴子已经被wesker1999于2009-3-10 5:26:08编辑过]

顶，好书，现在才敢买这些稍贵点的书啊

太贵了，还免费？

,是免费的

Summary: handy reference guide
Rating: 4

Evans et al offer a group of probability distribution functions. Each is given a few pages in which it is described. Often with formulas for the mean and variance and median. Sometimes, there are also expressions for the skewness and kurtosis. The conciseness of the descriptions make this a handy reference guide.

You should be clear on this. The book is not one to learn about these distributions for the first time. Of course, there are an infinite number of possible distributions. But the choices in the book are many that you are likely to run across in statistical applications.

Summary: Great overview - missing Levy flights; no index
Rating: 3

This is a very good overview of a variety of statistical distributions. I particularly like the empirical distribution, which gives a detailed method for constructing a distribution from empirical data.

However, it lacks some details that I am interested in such as the Levy distribution, robust comparisons between empirical and theoretical distributions, and a focus or discussion on distribution tails.

I knocked off another star because, incredibly, the book lacks and index.

Summary: Very helpful
Rating: 4

No book can possibly cover all distributions - new ones seem to show up in every new problem that arises. This book covers the common ones, maybe all the distributions a student sees in the first stats course or two.

The coverage is quite good for routine, and some non-routine purposes. I find the characteristic functions especially helpful. Each distribution's description of how it arises is also very useful - it's the kind of information that a practitioner needs in order to apply distributions to problems in meaningful ways.

I know that no book can say everything, but a few additions would have improved this book significantly. More discussion of applications would have helped. So would a discussion of general techniques for generating random numbers - inverse distributions, rejection, etc.

The two real weaknesses I found were in the extreme value and the empirical distributions. Extreme values don't stand alone. They often arise in ways dependent on other distributions. An extreme value distribution might describe the results of many experiments that find the largest of N values drawn from distribution P - with different results according to P. These distributions don't have convenient closed forms, but are amenable to some kinds of analysis anyway.

Perhaps the authors do a reasonable job of empirical distributions in the continuous case, but discrete (categorical) cases arise more in my work. Discrete distributions must answer such questions as: given that my sampling may not have found objects of all possible types, how many unknown types are probably still out there? Lots of problems have distributions too complicated for analysis or too poorly understood for book formulas to work, and must be handled empirically. More discussion of empirical techniques would make this a much stronger reference.

Despite its soft spots, this is a very practical reference. I expect it to be a productive member of my technical library.

Summary: concise handbook
Rating: 4

This is an extremely valuable compendium of what almost any pracitioner needs to know about 40 of the most commonly used statistical distributions. It is designed as a quick lookup reference for each of the distributions. Most chapters begin with a few brief lines describing some of the applications of the distribution, and then provide a list of relevant formulae, such as for the distribution function, probability density, moments etc. Relationships to other distributions are defined, means of estimating the parameters provided, and ways of generating random numbers from the distribution are indicated.
Graphs of the distributions are shown with varying parameter values in most cases.

The book should be seen purely as a handbook on statistical distributions, not as a theoretical reference. The book is ideal for those who make use of statistical distributions in other fields, and who are not necessarily statisticians themselves. I have no formal statistics training, but use distributions extensively in my own work, and found this book very easy to understand. I have been using Johnson and Kotz monographs fairly extensively as references for the distributions in which I am interested, but find this book a much simpler reference for the basic facts of the distributions. In addition, its consistent use of notation across the chapters makes it much easier for the reader to cross reference.

I refrain from giving 5 stars to the book because of a few weaknesses, primarily omissions. Firstly, as an earlier reviewer pointed out, the lack of an index is a little annoying sometimes. Secondly, the bibliography is very slim, and so the reader interested in finding further details, proofs etc., is given very little direction. Thirdly, there are a few obvious omissions, such as the cumulative distribution function for the chi-squared distribution. Fourthly, random number generation is described only when the generation is relatively simple (for example, a method for generating random variates from a gamma distribution is described only for special cases). Finally, I would like to have seen more guidance provided in the sections on parameter estimation, such as first and second derivatives of log-likelihood functions when the estimates have to be derived iteratively.

Summary: the only book you'll ever need on distributions
Rating: 5

This is the most thorough reference on distributions that I have found. The information contained about each distribution is concisely stated in a few pages - you would probably have to look in several books to get the same material. Most useful to people writing digital simulations is instructions on how to generate the distribution using random number generators. This is especially useful if you don't have access to statistical software packages. Lack of an index detracts, but is minor. Listings are alphabetical, by distribution name, so you might have to page through the book to find one that is not in an obvious location (like continuous uniform is listed as "rectangular", but discrete uniform is listed as "discrete uniform"). You need to be familiar with basic statistics to understand the book; but you don't have to be a statistician.

什么时候贴上来

谢谢楼主

Great, many thx!