Applied Multivariate Statistical Analysis 下载

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

这本书应该都知道吧，不过多解释了，我这个可不是拿扫描仪影印版本，是出版社直接制作的电子版。一本书500多页10M。多元统计的学的圣经，不解释，大家快点下载吧~

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

分享0 收藏2 回帖

关键词：Multivariate Statistical multivariat statistica statistic 扫描仪 出版社 电子版 圣经 统计

这是第几版啊？ 恐怕这又是重复上传资料吧， 论坛上已经有免费的了，搜搜看看了
http://www.pinggu.org/bbs/viewthread.php?tid=579135
http://www.pinggu.org/bbs/viewthread.php?tid=831768

2# 99rabbit
你看看大小就知道了，我这个不是他们那种扫描版的，那种版本打印出来根本没有办法阅读的。我这个是出版社直接给的影印版。绝对物有所值的

这么好的东西没有人下载，这个打印出来很漂亮的，绝对比那些影印版好太多了，如果有兴趣我可以拍下来自己打印的给大家看看，至于这本书，不用我解释了，多元统计学这本应该算不错了，中大和华工n个师兄强烈推荐，这个版本是我软磨硬泡才拿过来了。。。。。。。。。。。

绝对不是重复的资料，内容肯定是大同小易的，你看看附件的大小就知道了，2个文件随便一个就可以了，我上传了2次，重复了

作者是哪位？第几版？

second edition

wolfgang

这么经典的东西竟然没人下载。。。。。。。。。。。

Wolfgang H?rdle · Léopold Simar
Applied Multivariate Statistica lAnalysis
Wol fgang H?rdle

Humboldt-Universit?t zu Berlin
CASE - Center for Applied Statistics and Economics
Institut für Statistik und ?konometrie
Spandauer Stra?e 1
10178 Berlin, Germany
e-mail: haerdle@wiwi.hu-berlin.de
Léopold Simar
Université Catholique Louvain
Inst. Statistique
voie du Roman Payas 20
1348 Louvain-la-Neuve, Belgium
e-mail: simar@stat.ucl.ac.be


SPRINGER 好似论坛早已有了，大家可以搜搜。

Preface to the 2nd Edition
The second edition of this book widens the scope of the methods and applications of Applied
Multivariate Statistical Analysis. We have introduced more up to date data sets in our
examples. These give the text a higher degree of timeliness and add an even more applied
?avour. Since multivariate statistical methods are heavily used in quantitative ?nance and
risk management we have put more weight on the presentation of distributions and their
densities.
We discuss in detail di?erent families of heavy tailed distributions (Laplace, Generalized Hy-
perbolic). We also devoted a section on copulae, a new concept of dependency used in the
?nancial risk management and credit scoring. In the chapter on computer intensive meth-
ods we have added support vector machines, a new classi?cation technique from statistical
learning theory. We apply this method to bankruptcy and rating analysis of ?rms. The very
important CART (Classi?cation and Regression Tree) technique is also now inserted into
this chapter. We give an application to rating of companies.


我就是好久好久之前从人大经济论坛下载的，这是第二版的说明。

终于碰到懂得人了，这么好的书竟然没人下载，难道没有人看外文的书籍的？

1 Comparison of Batches
Multivariate statistical analysis is concerned with analyzing and understanding data in high
dimensions. We suppose that we are given a set {xi}n
i=1 of n observations of a variable vector
X in Rp. That is, we suppose that each observation xi has p dimensions:
xi = (xi1, xi2, ..., xip),
and that it is an observed value of a variable vector X ∈ Rp. Therefore, X is composed of p
random variables:
X = (X1,X2, ...,Xp)
where Xj, for j = 1, . . . , p, is a one-dimensional random variable. How do we begin to
analyze this kind of data? Before we investigate questions on what inferences we can reach
from the data, we should think about how to look at the data. This involves descriptive
techniques. Questions that we could answer by descriptive techniques are:
 Are there components of X that are more spread out than others?
 Are there some elements of X that indicate subgroups of the data?
 Are there outliers in the components of X?
 How “normal” is the distribution of the data?
 Are there “low-dimensional” linear combinations of X that show “non-normal” behavior?
One difficulty of descriptive methods for high dimensional data is the human perceptional
system. Point clouds in two dimensions are easy to understand and to interpret. With
modern interactive computing techniques we have the possibility to see real time 3D rotations
and thus to perceive also three-dimensional data. A “sliding technique” as described in
H¨ardle and Scott (1992) may give insight into four-dimensional structures by presenting
dynamic 3D density contours as the fourth variable is changed over its range.
A qualitative jump in presentation difficulties occurs for dimensions greater than or equal to
5, unless the high-dimensional structure can be mapped into lower-dimensional components
Klinke and Polzehl (1995). Features like clustered subgroups or outliers, however, can be
detected using a purely graphical analysis.
In this chapter, we investigate the basic descriptive and graphical techniques allowing simple
exploratory data analysis. We begin the exploration of a data set using boxplots. A boxplot
is a simple univariate device that detects outliers component by component and that can
compare distributions of the data among different groups. Next several multivariate techniques
are introduced (Flury faces, Andrews’ curves and parallel coordinate plots) which
provide graphical