[分享]免费 Statistical Distributions by Evans

7947

收藏 2009-03-09

Statistical Distributions (3 edition) by Merran Evans , Nicholas Hastings , Brian Peacock 这是一本很好的参考书. 包括了基本的统计分布函数. 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"/>&nbsp;

303540.pdf
大小:(9.04 MB)

马上下载

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

[此贴子已经被作者于2009-3-14 5:10:25编辑过]

admin
&nbsp;金钱&nbsp;+100
&nbsp;好文章&nbsp;2009-3-9 7:38:48

附件列表

301466.zip

大小:7.45 MB

马上下载

[分享]免费Ebook Statistical Distributions by Merran Evans

本附件包括：

Statistical Distributions_Evans 3rd.rar

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

全部回复

oxprovidence

2009-3-9 08:47:00

楼主这真的是免费的吗……

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

逆水行舟

2009-3-9 09:20:00

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]

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

xmok77

2009-3-9 10:37:00

顶，好书，现在才敢买这些稍贵点的书啊

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

pinip

2009-3-9 14:21:00

太贵了，还免费？

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

anthust

2009-3-9 18:42:00

,是免费的

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

点击查看更多内容…

wesker1999

2009-3-10 05:24:00

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.