作者: JUST 時(shí)間: 2025-3-21 22:46 作者: decipher 時(shí)間: 2025-3-22 03:58 作者: ensemble 時(shí)間: 2025-3-22 06:23
Multivariate Distributionsation on the relationship between the variables can be made available. Only basic statistical theory was used to derive tests of independence or of linear relationships. In this chapter we give an introduction to the basic probability tools useful in statistical multivariate analysis.作者: 騙子 時(shí)間: 2025-3-22 09:49
Theory of the Multinormalistribution, since it is often a good approximate distribution in many situations. Another reason for considering the multinormal distribution relies on the fact that it has many appealing properties: it is stable under linear transforms, zero correlation corresponds to independence, the marginals a作者: 漂浮 時(shí)間: 2025-3-22 14:55 作者: 不出名 時(shí)間: 2025-3-22 20:18 作者: 名次后綴 時(shí)間: 2025-3-22 21:41
Decomposition of Data Matrices by Factorsvariate or univariate devices used to reduce the dimensions of the observations. In the following three chapters, issues of reducing the dimension of a multivariate data set will be discussed. The perspectives will be different but the tools will be related.作者: 追逐 時(shí)間: 2025-3-23 03:45 作者: Consensus 時(shí)間: 2025-3-23 09:14 作者: 有罪 時(shí)間: 2025-3-23 09:57 作者: NICE 時(shí)間: 2025-3-23 15:56 作者: 拍翅 時(shí)間: 2025-3-23 18:36
Correspondence Analysisry frequency table where the joint frequencies of two qualitative variables are reported. For instance a (2×2) table could be formed by observing from a sample of . individuals two qualitative variables: the individual’s sex and whether the individual smokes. The table reports the observed joint fre作者: 比目魚 時(shí)間: 2025-3-24 00:19
Canonical Correlation Analysist type of low-dimensional projection helps in finding possible joint structures for the two samples. The canonical correlation analysis is a standard tool of multivariate statistical analysis for discovery and quantification of associations between two sets of variables.作者: fertilizer 時(shí)間: 2025-3-24 06:15 作者: Distribution 時(shí)間: 2025-3-24 10:16
ications in different fields..The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features.A new Chapter on Regression Models has been added.All numerical exampl作者: 樸素 時(shí)間: 2025-3-24 13:10 作者: sed-rate 時(shí)間: 2025-3-24 18:48 作者: overwrought 時(shí)間: 2025-3-24 22:38 作者: inchoate 時(shí)間: 2025-3-25 00:47 作者: Acumen 時(shí)間: 2025-3-25 05:16
Entwicklung von Data-Warehouse-Systemen data set will be presented. In this chapter, as a starting point, simple and basic tools are used to describe dependency. They are constructed from elementary facts of probability theory and introductory statistics (for example, the covariance and correlation between two variables).作者: prodrome 時(shí)間: 2025-3-25 08:20 作者: 無目標(biāo) 時(shí)間: 2025-3-25 14:24
Entwicklung von Data-Warehouse-Systemenl scientist than the observed quantitative measures (.) themselves, because they give a better understanding of the behavior of households. As shown in the examples below, the same kind of factor analysis is of interest in many fields such as psychology, marketing, economics, politic sciences, etc.作者: Laconic 時(shí)間: 2025-3-25 18:08
Entwicklung von Data-Warehouse-Systemenpriate criterion. Once the clusters are obtained, it is generally useful to describe each group using some descriptive tool from Chapters?., . or?. to create a better understanding of the differences that exist among the formulated groups.作者: NIP 時(shí)間: 2025-3-25 20:35
Moving to Higher Dimensions data set will be presented. In this chapter, as a starting point, simple and basic tools are used to describe dependency. They are constructed from elementary facts of probability theory and introductory statistics (for example, the covariance and correlation between two variables).作者: amygdala 時(shí)間: 2025-3-26 00:33 作者: 音樂學(xué)者 時(shí)間: 2025-3-26 04:24
Factor Analysisl scientist than the observed quantitative measures (.) themselves, because they give a better understanding of the behavior of households. As shown in the examples below, the same kind of factor analysis is of interest in many fields such as psychology, marketing, economics, politic sciences, etc.作者: 悠然 時(shí)間: 2025-3-26 11:33 作者: Incise 時(shí)間: 2025-3-26 13:38
Entwicklung von Data-Warehouse-Systemenustomers (including for example age, salary, marital status, the amount of the loan, etc.). The new customer is a new observation . with the same variables. The discrimination rule has to classify the customer into one of the two existing groups and the discriminant analysis should evaluate the risk of a possible “bad decision”.作者: indecipherable 時(shí)間: 2025-3-26 17:01 作者: 原來 時(shí)間: 2025-3-26 22:24 作者: 一大群 時(shí)間: 2025-3-27 04:24 作者: 牽索 時(shí)間: 2025-3-27 08:05
atment of the subject A wealth of examples and exercises – i.Most of the observable phenomena in the empirical sciences are of a multivariate nature.? In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indic作者: 止痛藥 時(shí)間: 2025-3-27 11:51 作者: incredulity 時(shí)間: 2025-3-27 14:34 作者: Moderate 時(shí)間: 2025-3-27 20:29
Entwicklung von Data-Warehouse-Systemen a sample of . individuals two qualitative variables: the individual’s sex and whether the individual smokes. The table reports the observed joint frequencies. In general (.×.) tables may be considered.作者: Pelvic-Floor 時(shí)間: 2025-3-28 01:41 作者: Migratory 時(shí)間: 2025-3-28 06:01
Theory of the Multinormalon the fact that it has many appealing properties: it is stable under linear transforms, zero correlation corresponds to independence, the marginals and all the conditionals are also multivariate normal variates, etc. The mathematical properties of the multinormal make analyses much simpler.作者: 長矛 時(shí)間: 2025-3-28 08:46 作者: 無法取消 時(shí)間: 2025-3-28 12:23 作者: 邊緣 時(shí)間: 2025-3-28 16:21
Multidimensional Scalinguct A is better than product B”. High-dimensional observations therefore often have mixed data characteristics and contain relative information (w.r.t. a defined standard) rather than absolute coordinates that would enable us to employ one of the multivariate techniques presented so far.作者: condone 時(shí)間: 2025-3-28 20:41 作者: 多余 時(shí)間: 2025-3-29 01:54 作者: guardianship 時(shí)間: 2025-3-29 07:01
Wolfgang Karl H?rdle,Léopold SimarRevised and updated third edition offers a broader range of material Wide scope of methods and applications, making this a comprehensive treatment of the subject A wealth of examples and exercises – i作者: 無聊的人 時(shí)間: 2025-3-29 09:20 作者: COMA 時(shí)間: 2025-3-29 14:24
A Short Excursion into Matrix Algebranotations used in this book for vectors and matrices. Eigenvalues and eigenvectors play an important role in multivariate techniques. In Sections?. and?., we present the spectral decomposition of matrices and consider the maximisation (minimisation) of quadratic forms given some constraints.作者: LIKEN 時(shí)間: 2025-3-29 17:11 作者: CLAP 時(shí)間: 2025-3-29 22:54
Decomposition of Data Matrices by Factorsvariate or univariate devices used to reduce the dimensions of the observations. In the following three chapters, issues of reducing the dimension of a multivariate data set will be discussed. The perspectives will be different but the tools will be related.作者: 翅膀拍動(dòng) 時(shí)間: 2025-3-30 01:12
Canonical Correlation Analysist type of low-dimensional projection helps in finding possible joint structures for the two samples. The canonical correlation analysis is a standard tool of multivariate statistical analysis for discovery and quantification of associations between two sets of variables.作者: 驚呼 時(shí)間: 2025-3-30 07:55 作者: Blazon 時(shí)間: 2025-3-30 11:58
Entwicklung von Data-Warehouse-Systemenation on the relationship between the variables can be made available. Only basic statistical theory was used to derive tests of independence or of linear relationships. In this chapter we give an introduction to the basic probability tools useful in statistical multivariate analysis.作者: 愛了嗎 時(shí)間: 2025-3-30 14:50 作者: OGLE 時(shí)間: 2025-3-30 20:35
Entwicklung von Data-Warehouse-Systement type of low-dimensional projection helps in finding possible joint structures for the two samples. The canonical correlation analysis is a standard tool of multivariate statistical analysis for discovery and quantification of associations between two sets of variables.作者: JIBE 時(shí)間: 2025-3-31 00:05
https://doi.org/10.1007/978-3-658-20073-2ervations of a variable vector . in .. That is, we suppose that each observation .. has . dimensions: . and that it is an observed value of a variable vector .. Therefore, . is composed of . random variables: . where .., for .=1,…,., is a one-dimensional random variable. How do we begin to analyse t作者: Friction 時(shí)間: 2025-3-31 03:34
Entwicklung von Controlleraufgabennotations used in this book for vectors and matrices. Eigenvalues and eigenvectors play an important role in multivariate techniques. In Sections?. and?., we present the spectral decomposition of matrices and consider the maximisation (minimisation) of quadratic forms given some constraints.作者: 橡子 時(shí)間: 2025-3-31 08:15
