作者: Pelvic-Floor 時間: 2025-3-21 23:56 作者: 填料 時間: 2025-3-22 04:08 作者: annexation 時間: 2025-3-22 08:23 作者: 噴出 時間: 2025-3-22 10:48 作者: infinite 時間: 2025-3-22 14:50 作者: 虛假 時間: 2025-3-22 20:46 作者: Armada 時間: 2025-3-22 22:28 作者: 矛盾心理 時間: 2025-3-23 05:10
Hypothesis TestingIn the preceding chapter, the theoretical basis of estimation theory was presented. Now we turn our interest towards testing issues: we want to test the hypothesis . that the unknown parameter . belongs to some subspace of ?.. This subspace is called the . and will be denoted by Ω. ? ∝..作者: Allodynia 時間: 2025-3-23 05:55
Entwicklung und Situation des Baumarktes observations 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 be作者: 維持 時間: 2025-3-23 10:05 作者: 失望未來 時間: 2025-3-23 14:53
https://doi.org/10.1007/978-3-662-01075-4tools were based on either univariate (bivariate) data representations or on “slick” transformations of multivariate information perceivable by the human eye. Most of the tools are extremely useful in a modelling step, but unfortunately, do not give the full picture of the data set. One reason for t作者: 收集 時間: 2025-3-23 20:51 作者: graphy 時間: 2025-3-24 00:52 作者: airborne 時間: 2025-3-24 03:15 作者: 媽媽不開心 時間: 2025-3-24 09:05
https://doi.org/10.1007/978-3-658-40755-1variate 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: epinephrine 時間: 2025-3-24 13:49
https://doi.org/10.1007/978-3-658-40755-1 Principal components analysis has the same objective with the exception that the rows of the data matrix . will now be considered as observations from a .-variate random variable .. The principle idea of reducing the dimension of . is achieved through linear combinations. Low dimensional linear com作者: Isolate 時間: 2025-3-24 18:28 作者: LASH 時間: 2025-3-24 20:43
https://doi.org/10.1007/978-3-658-41831-1situations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and metho作者: 喧鬧 時間: 2025-3-24 23:34
https://doi.org/10.1007/978-3-031-36043-5 observations, into these known groups. For instance, in credit scoring, a bank knows from past experience that there are good customers (who repay their loan without any problems) and bad customers (who showed difficulties in repaying their loan). When a new customer asks for a loan, the bank has t作者: 無辜 時間: 2025-3-25 04:10
https://doi.org/10.1007/978-3-031-36043-5ry 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 f作者: 把手 時間: 2025-3-25 07:56
Die hausarztzentrierte Versorgungt Analysis are dominantly used tools. In many applied sciences data is recorded as ranked information. For example, in marketing, one may record “product A is better than product B”. High-dimensional observations therefore often have mixed data characteristics and contain relative information (w.r.t作者: CREEK 時間: 2025-3-25 14:18 作者: heckle 時間: 2025-3-25 17:35
A Short Excursion into Matrix Algebrans used in this book for vectors and matrices. Eigenvalues and eigenvectors play an important role in multivariate techniques. In Sections 2.2 and 2.3, we present the spectral decomposition of matrices and consider the maximization (minimization) of quadratic forms given some constraints.作者: Diskectomy 時間: 2025-3-25 22:42 作者: 一加就噴出 時間: 2025-3-26 03:57
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: angina-pectoris 時間: 2025-3-26 04:29 作者: 受人支配 時間: 2025-3-26 12:15
Comparison of Batches observations 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 be作者: SOBER 時間: 2025-3-26 13:09 作者: Ptsd429 時間: 2025-3-26 19:42
Moving to Higher Dimensionstools were based on either univariate (bivariate) data representations or on “slick” transformations of multivariate information perceivable by the human eye. Most of the tools are extremely useful in a modelling step, but unfortunately, do not give the full picture of the data set. One reason for t作者: 懶鬼才會衰弱 時間: 2025-3-26 21:03
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: 淺灘 時間: 2025-3-27 01:16
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: 迷住 時間: 2025-3-27 09:02
Theory of Estimation generates the data. This is known as statistical inference: we infer from information contained in a sample properties of the population from which the observations are taken. In multivariate statistical inference, we do exactly the same. The basic ideas were introduced in Section 4.5 on sampling t作者: Expostulate 時間: 2025-3-27 13:29
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: 代理人 時間: 2025-3-27 17:28 作者: 和音 時間: 2025-3-27 20:51 作者: 固執(zhí)點好 時間: 2025-3-27 23:42
Cluster Analysissituations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and metho作者: 清楚 時間: 2025-3-28 04:14 作者: 名詞 時間: 2025-3-28 10:10 作者: confederacy 時間: 2025-3-28 12:34
Multidimensional Scalingt Analysis are dominantly used tools. In many applied sciences data is recorded as ranked information. For example, in marketing, one may record “product A is better than product B”. High-dimensional observations therefore often have mixed data characteristics and contain relative information (w.r.t作者: 易碎 時間: 2025-3-28 16:35 作者: 佛刊 時間: 2025-3-28 22:24
Comparison of Batchesgin 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:作者: FLUSH 時間: 2025-3-29 02:51 作者: Chronological 時間: 2025-3-29 04:09
Conjoint Measurement Analysisase in overall utility. The Conjoint Measurement Analysis is a method for attributing utilities to the components (part worths) on the basis of ranks given to different outcomes (stimuli) of the product. An important assumption is that the overall utility is decomposed as a sum of the utilities of the components.作者: 拔出 時間: 2025-3-29 09:50
Entwicklung und Situation des Baumarktesgin 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:作者: Keratin 時間: 2025-3-29 14:55
https://doi.org/10.1007/978-3-658-40755-1binations are often easier to interpret and serve as an intermediate step in a more complex data analysis. More precisely one looks for linear combinations which create the largest spread among the values of .. In other words, one is searching for linear combinations with the largest variances.作者: Mere僅僅 時間: 2025-3-29 16:05
Analyse Zeitfenster 2 (2002-2005)ase in overall utility. The Conjoint Measurement Analysis is a method for attributing utilities to the components (part worths) on the basis of ranks given to different outcomes (stimuli) of the product. An important assumption is that the overall utility is decomposed as a sum of the utilities of the components.作者: 殺子女者 時間: 2025-3-29 22:22
https://doi.org/10.1007/978-3-031-36043-5stomers (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: 下級 時間: 2025-3-30 02:52 作者: outskirts 時間: 2025-3-30 07:33 作者: plasma 時間: 2025-3-30 08:43
l: .Most of the observable phenomena in the empirical sciences are of a multivariate nature.In financial studies, assets in stock markets are observed simultaneously and their joint development is analyzed to better understand general tendencies and to track indices. In medicine recorded observation作者: geriatrician 時間: 2025-3-30 13:36
Konzeptentwicklung in der Pflegepraxis,on 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: onlooker 時間: 2025-3-30 18:38
https://doi.org/10.1007/978-3-031-36043-5om 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: excrete 時間: 2025-3-30 23:54 作者: Lipoma 時間: 2025-3-31 04:52
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: 發(fā)出眩目光芒 時間: 2025-3-31 06:39
Correspondence Analysisom 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: MAZE 時間: 2025-3-31 10:42
