- admissible
- Bayes
- agglomerative techniques
- Hierarchical
- allocation rules
- 12.1
- Andrews' curves
- 1.6
| 1.6
- angle between two vectors
- Angle
- ANOVA
- 3.5
- ANOVA - simple analysis of variace
- 3.5
- Bayes discriminant rule
- Bayes
- Bernoulli distribution
- 4.5
- Bernoulli distributions
- 4.5
- best line
- Subspaces
- binary structure
- Similarity
- Biplots
- Biplots
- bootstrap
- 4.6
| 4.6
- bootstrap sample
- 4.6
| 4.6
- Boston housing
- 1.8
| 3.7
| 7.3
| 9.8
| 10.4
| 11.4
| 12.3
- boxplot
- 1.1
- construction
- Construction
- canonical correlation
- 14.
- canonical correlation analysis
- 14.
- canonical correlation coefficient
- 14.1
- canonical correlation variable
- 14.1
- canonical correlation vector
- 14.1
- centering matrix
- 3.3
- central limit theorem (CLT)
- 4.5
| 4.5
| 4.5
- centroid
- Hierarchical
- characteristic functions
- 4.2
| no title
- classic blue pullovers
- 3.1
- cluster algorithms
- 11.3
- cluster analysis
- 11.
- Cochran theorem
- 5.2
- coefficient of determination
- 3.4
| 3.6
- corrected
- 3.6
- column space
- no title
| 8.1
- common factors
- 10.1
- common principal components
- 9.7
- communality
- 10.1
- complete linkage
- Hierarchical
- computationally intensive techniques
- 18.
- concentration ellipsoid
- Geometry
- conditional approximations
- Conditional
- conditional covariance
- SIR
| SIR
- conditional density
- 4.1
- conditional distribution
- 5.1
- conditional expectation
- Conditional
| 18.3
| SIR
- conditional pdf
- 4.1
- confidence interval
- 4.5
- confussion matrix
- Estimation
- conjoint measurement analysis
- 16.
- contingency table
- 13.
- contrast
- Repeated
- convex hull
- 18.1
- copula
- 4.1
- correlation
- 3.2
- multiple
- Conditional
- correspondence analysis
- 13.
| 13.
- covariance
- 3.1
- covariance matrix
- decomposition
- 9.1
- properties
- no title
- CPCA
- 9.7
- Cramer-Rao
- 6.2
- Cramer-Rao-lower bound
- 6.2
- Cramer-Wold
- Characteristic
- cumulant
- Cumulant
- cumulative distribution function (cdf)
- 4.1
- curse of dimensionality
- 18.3
- data depth
- 18.1
- degrees of freedom
- 3.5
- dendrogram
- Hierarchical
- density estimates
- 1.2
- density functions
- 4.1
- derivatives
- 2.4
- determinant
- Determinant
- diagonal matrix
- Properties
- Dice
- Similarity
- discriminant analysis
- 12.
- discriminant rule
- 12.1
- discrimination rules in practice
- 12.2
- dissimilarity of cars
- 15.1
- distance
- d
- Distance
- Euclidean
- Distance
- iso-distance curves
- Distance
- distance matrix
- 15.2.1
- distance measures
- Distance
- distribution
- 4.1
- draftman's plot
- 1.4
- duality relations
- 8.4
- duality theorem
- Relation
- effective dimension reduction directions
- 18.3
| The
- effective dimension reduction space
- 18.3
- efficient portfolio
- 17.2
- eigenvalues
- no title
- eigenvectors
- no title
- elliptical distribution
- 5.4
- elliptically symmetric distribution
- 18.3
- estimation
- 6.
- existence of a riskless asset
- Existence
- expected cost of misclassification
- Maximum
- exploratory projection pursuit
- Exploratory
- extremes
- 1.1
- F-spread
- 1.1
- f-test
- The
- faces
- 1.5
| 1.5
- factor analysis
- 10.
- factor analysis model
- 10.1
- factor model
- 10.2
- factor score
- 10.3
- factor scores
- 10.3
- factorial axis
- Representation
| Subspaces
- factorial method
- 9.6
- factorial representation
- 8.5
| 8.5
- factorial variable
- Representation
| 8.5
- factors
- 8.1
- Farthest Neighbor
- Hierarchical
- Fisher information
- 6.2
- Fisher information matrix
- 6.2
| 6.2
- Fisher's linear discrimination function
- Fisher's
- five-number summary
- 1.1
| 1.1
- flury faces
- 1.5
- fourths
- 1.1
- French food expenditure
- Quality
- full model
- 3.5
- G-inverse
- G-inverse
- non-uniqueness
- 2.2
- general multinormal distribution
- 5.3
- gradient
- 2.4
- group-building algorithm
- 11.1
- Hessian
- 2.4
- hierarchical algorithm
- Hierarchical
- histograms
- 1.2
| 1.2
- Hotelling
-distribution
- 5.3
- idempotent matrix
- Properties
- identity matrix
- Properties
- independence copula
- 4.1
- independent
- 3.2
| 4.1
- inertia
- 8.5
| 8.5
- information matrix
- 6.2
- interpretation of the factors
- Interpretation
- interpretation of the principal components
- 9.3
- invariance of scale
- Invariance
- inverse
- Inverse
- inverse regression
- 18.3
| SIR
- Jaccard
- Similarity
- Jacobian
- 4.3
- Jordan decomposition
- 2.2
| 2.2
- kernel densities
- 1.3
| 1.3
- kernel estimator
- 1.3
- Kulczynski
- Similarity
- likelihood function
- 6.1
- likelihood ratio test
- 7.1
- limit theorems
- 4.5
- linear discriminant analysis
- Maximum
- linear regression
- 3.4
- linear transformation
- no title
- link function
- 18.3
- loadings
- 10.1
| 10.1
- non-uniqueness
- Non-Uniqueness
- log-likelihood function
- 6.1
- Mahalanobis distance
- Maximum
- Mahalanobis transformation
- Mahalanobis
| 4.4
| 4.4
- marginal densities
- 4.1
- marketing strategies
- 3.5
- maximum likelihood discriminant rule
- Maximum
- maximum likelihood estimator
- 6.1
- MDS direction
- 15.1
- mean-variance
- 17.
| 17.2
- median
- 1.1
| 18.1
- metric methods
- 15.1
- moments
- 4.2
| no title
- multidimentional scaling
- 15.
- multinormal
- Geometry
| 5.
| 5.
- multinormal distribution
- 4.4
- multivariate distributions
- 4.
- multivariate median
- 18.1
- multivariate t-distribution
- 5.4
- Nearest Neighbor
- Hierarchical
- non-metric solution
- 16.3
- Nonexistence of a riskless asset
- Nonexistence
- nonhomogeneous
- Linear
- nonmetric methods of MDS
- 15.1
- norm of a vector
- Norm
- normal distribution
- 6.1
- normalized principal components (NPCs)
- 9.5
- null space
- no title
- order statistics
- 1.1
- orthogonal complement
- Projection
- orthogonal matrix
- Properties
- orthonormed
- Subspaces
- outliers
- 1.
- outside bars
- 1.1
- parallel coordinates plots
- 1.7
| 1.7
- parallel profiles
- Parallel
- partitioned covariance matrix
- 5.1
- partitioned matrices
- 2.5
- PAV algorithm
- 15.3.1
| Nonmetric
- pool-adjacent violators algorithm
- 15.3.1
| Nonmetric
- portfolio analysis
- 17.
- portfolio choice
- 17.1
- positive definiteness
- Definiteness
- positive or negative dependence
- 1.4
- positive semidefinite
- 3.3
- principal components transformation
- 9.1
- principal axes
- Distance
- principal component method
- The
- principal components
- 9.1
- principal components analysis (PCA)
- 9.
| 18.3
| The
- principal components in practice
- 9.2
- principal components technique
- 9.2
- principal components transformation
- 9.1
- principal factors
- The
- profile analysis
- Profile
- profile method
- 16.2
- projection matrix
- Projection
- projection pursuit
- 18.2
- projection pursuit regression
- Projection
- projection vector
- 18.3
- proximity between objects
- 11.2
- proximity measure
- 11.1
- quadratic discriminant analysis
- Classification
- quadratic form
- 2.3
- quadratic forms
- no title
- quality of the representations
- Quality
- randomized discriminant rule
- Bayes
- rank
- Rank
- reduced model
- 3.5
- rotation
- Rotation
- rotations
- no title
- row space
- 8.1
- Russel and Rao (RR)
- Similarity
- sampling distributions
- 4.5
- scatterplot matrix
- 1.4
- scatterplots
- 1.4
- separation line
- 1.4
- similarity of objects
- Similarity
- Simple Matching
- Similarity
- single linkage
- Hierarchical
- single matching
- Similarity
- singular normal distribution
- Singular
- singular value decomposition (SVD)
- 2.2
| 8.4
- sliced inverse regression
- 18.3
| The
| The
- algorithm
- The
- sliced inverse regression II
- SIR
| SIR
| SIR
| The
| The
| The
- algorithm
- The
- solution
- nonmetric
- Nonmetric
- specific factors
- 10.1
- specific variance
- 10.1
- spectral decompositions
- 2.2
- spherical distribution
- 5.4
- standardized linear combinations (SLC)
- 9.1
- statistics
- 4.5
- stimulus
- 16.2
- Student's t-distribution
- 3.4
- sum of squares
- 3.5
- summary statistics
- 3.3
- Swiss bank data
- 1.1
- symmetric matrix
- Properties
- t-test
- 3.4
- Tanimoto
- Similarity
- testing
- 7.
- The CAPM
- 17.4
- trace
- Trace
- trade-off analysis
- 16.2
- transformations
- 4.3
- transpose
- Transpose
- two factor method
- 16.2
- unbiased estimator
- 6.2
- uncorrelated factors
- 10.1
- unit vector
- Norm
- upper triangular matrix
- Properties
- variance explained by PCs
- Variance
- varimax criterion
- Rotation
- varimax method
- Rotation
- varimax rotation method
- Rotation
- Ward clustering
- Hierarchical
- Wishart distribution
- 5.2
| 5.2