Linear models in statistics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley-Interscience
2008
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 672 S. graph. Darst. |
| ISBN: | 9780471754985 0471754986 |
Internformat
MARC
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| 100 | 1 | |a Rencher, Alvin C. |d 1934- |e Verfasser |0 (DE-588)136269230 |4 aut | |
| 245 | 1 | 0 | |a Linear models in statistics |c Alvin C. Rencher and G. Bruce Schaalje |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley-Interscience |c 2008 | |
| 300 | |a XVI, 672 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Linear models (Statistics) | |
| 650 | 0 | 7 | |a Lineares Modell |0 (DE-588)4134827-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Lineares Modell |0 (DE-588)4134827-8 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Schaalje, G. Bruce |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015862554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015862554 | ||
Datensatz im Suchindex
| _version_ | 1804136836127260672 |
|---|---|
| adam_text | CONTENTS
Preface
xiii
1
Introduction
1
1.1
Simple Linear Regression Model
1
1.2
Multiple Linear Regression Model
2
1.3
Analysis-of-Variance Models
3
2
Matrix Algebra
5
2.1
Matrix and Vector Notation
5
2.1.1
Matrices, Vectors, and Scalars
5
2.1.2
Matrix Equality
6
2.1.3
Transpose
7
2.1.4
Matrices of Special Form
7
2.2
Operations
9
2.2.1
Sum of Two Matrices or Two Vectors
9
2.2.2
Product of a Scalar and a Matrix
10
2.2.3
Product of Two Matrices or Two Vectors
10
2.2.4
Hadamard
Product of Two
Matrices or Two Vectors
16
2.3
Partitioned Matrices
16
2.4
Rank
19
2.5
Inverse
21
2.6
Positive Definite Matrices
24
2.7
Systems of Equations
28
2.8
Generalized Inverse
32
2.8.1
Definition and Properties
33
2.8.2
Generalized Inverses and Systems of Equations
36
2.9
Determinants
37
2.10
Orthogonal Vectors and Matrices
41
2.11
Trace
44
2.12
Eigenvalues and Eigenvectors
46
2.12.1
Definition
46
2.12.2
Functions of a Matrix
49
vi
CONTENTS
2.12.3
Products
50
2.12.4 Symmetrie
Matrices
51
2.12.5
Positive
Definite and Semidefinite
Matrices
53
2.13
Idempotent
Matrices
54
2.14
Vector and
Matrix
Calculus
56
2.14.1
Derivatives of Functions of Vectors and Matrices
56
2.14.2
Derivatives Involving Inverse Matrices and Determinants
58
2.14.3
Maximization or Minimization of a Function of a Vector
60
3
Random Vectors and Matrices
69
3.
1 Introduction
69
3.2
Means, Variances, Covariances, and Correlations
70
3.3
Mean Vectors and Covariance Matrices for Random Vectors
75
3.3.1
Mean Vectors
75
3.3.2
Covariance Matrix
75
3.3.3
Generalized Variance
77
3.3.4
Standardized Distance
77
3.4
Correlation Matrices
77
3.5
Mean Vectors and Covariance Matrices for
Partitioned Random Vectors
78
3.6
Linear Functions of Random Vectors
79
3.6.1
Means
80
3.6.2
Variances and Covariances
81
4
Multivariate Normal Distribution
87
4.1
Univariate Normal Density Function
87
4.2
Multivariate Normal Density Function
88
4.3
Moment Generating Functions
90
4.4
Properties of the Multivariate Normal Distribution
92
4.5
Partial Correlation
100
5
Distribution of Quadratic Forms in
y
105
5.1
Sums of Squares
105
5.2
Mean and Variance of Quadratic Forms
107
5.3
Noncentral Chi-Square
Distribution
112
5.4
Noncentral F
and
/
Distributions
114
5.4.1
Noncentral
Γ
Distribution
114
5.4.2
Noncentral
/
Distribution
116
5.5
Distribution of Quadratic Forms
117
5.6
Independence of Linear Forms and Quadratic Forms
119
CONTENTS
vii
6
Simple Linear Regression
127
6.1
The Model
127
6.2
Estimation of /30,
ß ,
and
σ2
128
6.3
Hypothesis Test and Confidence Interval for J3j
132
6.4
Coefficient of Determination
133
7
Multiple Regression: Estimation
137
7.1
Introduction
137
7.2
The Model
137
7.3
Estimation of
β
and
σ~
141
7.3.1
Least-Squares Estimator for
β
145
7.3.2
Properties of the Least-Squares Estimator
β
141
7.3.3
An Estimator for
cr2
149
7.4
Geometry of Least-Squares
151
7.4.1
Parameter Space, Data Space, and Prediction Space
152
7.4.2
Geometric Interpretation of the Multiple
Linear Regression Model
153
7.5
The Model in Centered Form
154
7.6
Normal Model
157
7.6.1
Assumptions
157
7.6.2
Maximum Likelihood Estimators for
β
and
σ2
158
7.6.3
Properties of
β
and
σ2
159
7.7
R2
in Fixed-x Regression
161
7.8
Generalized Least-Squares: cov(y)
=
cr V
164
7.8.1
Estimation of
β
and
σ2
when cov(y)
=
σ2
164
7.8.2
Misspecification of the Error Structure
167
7.9
Model Misspecification
169
7.10
Orthogonalization
174
8
Multiple Regression: Tests of Hypotheses
and Confidence Intervals
185
8.1
Test of Overall Regression
185
8.2
Test on a Subset of the
β
Values
189
8.3
F
Test in Terms of R2
196
8.4
The General Linear Hypothesis Tests for
Яо:
C/3
= 0
and Ho.
Cß =
t
198
8.4.1
The Test for
Яо:
Cß = 0 198
8.4.2
The Test for
/ƒ„:
C/3
= / 203
8.5
Tests on
β,
and a/3
204
8.5.1
Testing One
ßj
or One
aß 204
8.5.2
Testing Several
β
or a ,/8 Values
205
v¡¡¡
CONTENTS
8.6
Confidence Intervals and Prediction Intervals
209
8.6.1
Confidence Region for
β
209
8.6.2
Confidence Interval for
/3, 210
8.6.3
Confidence Interval for a
β
211
8.6.4
Confidence Interval for E(y)
211
8.6.5
Prediction Interval for a Future Observation
213
8.6.6
Confidence Interval for
σ2
215
8.6.7
Simultaneous Intervals
215
8.7
Likelihood Ratio Tests
217
9
Multiple Regression: Model Validation and Diagnostics
227
9.1
Residuals
227
9.2
The Hat Matrix
230
9.3
Outliers
232
9.4
Influential Observations and Leverage
235
10
Multiple Regression: Random jc s
243
10.1
Multivariate Normal Regression Model
244
10.2
Estimation and Testing in Multivariate Normal Regression
245
10.3
Standardized Regression Coefficents
249
10.4
R in Multivariate Normal Regression
254
10.5
Tests and Confidence Intervals for R2
258
10.6
Effect of Each Variable on R2
262
10.7
Prediction for Multivariate Normal or
Nonnormal
Data
265
10.8
Sample Partial Correlations
266
11
Multiple Regression: Bayesian Inference
277
11.1
Elements of Bayesian Statistical Inference
277
11.2
A Bayesian Multiple Linear Regression Model
279
1
1.2.1
A Bayesian Multiple Regression Model
with a Conjugate Prior
280
11.2.2
Marginal Posterior Density of
β
282
11.2.3
Marginal Posterior Densities of
τ
and
σ2
284
11.3
Inference in Bayesian Multiple Linear Regression
285
1
1.3.1
Bayesian Point and Interval Estimates of
Regression Coefficients
285
11.3.2
Hypothesis Tests for Regression Coefficients
in Bayesian Inference
286
11.3.3
Special Cases of Inference in Bayesian Multiple
Regression Models
286
11.3.4
Bayesian Point and Interval Estimation of
σ2
287
CONTENTS
¡χ
11.4 Bayesian
Inference through Markov Chain
Monte Carlo Simulation
288
11.5
Posterior Predictive Inference
290
12
Analysis-of-Variance Models
295
12.1
Non-Full-Rank Models
295
12.1.1
One-Way Model
295
12.1.2
Two-Way Model
299
12.2
Estimation
301
12.2.1
Estimation of
β
302
12.2.2
Estimable Functions of
β
305
12.3
Estimators
309
12.3.1
Estimators of
λ β
309
12.3.2
Estimation of
σ2
313
12.3.3
Normal Model
314
12.4
Geometry of Least-Squares in the
Overparameterized Model
316
12.5
Reparameterization
318
12.6
Side Conditions
320
12.7
Testing Hypotheses
323
12.7.1
Testable Hypotheses
323
12.7.2
Full-Reduced-Model Approach
324
12.7.3
General Linear Hypothesis
326
12.8
An Illustration of Estimation and Testing
329
12.8.1
Estimable Functions
330
12.8.2
Testing a Hypothesis
331
12.8.3
Orthogonality of Columns of X
333
13
One-Way Analysis-of-Variance: Balanced Case
339
13.1
The One-Way Model
339
13.2
Estimable Functions
340
13.3
Estimation of Parameters
341
13.3.1
Solving the Normal Equations
341
13.3.2
An Estimator for
σ2
343
13.4
Testing the Hypothesis #():
μ. = μ2 =
■ ■ ■ =
μ
k
344
13.4.1
Full-Reduced-Model Approach
344
13.4.2
General Linear Hypothesis
348
13.5
Expected Mean Squares
351
13.5.1
Full-Reduced-Model Approach
352
13.5.2
General Linear Hypothesis
354
x
CONTENTS
13.6
Contrasts
357
13.6.1
Hypothesis Test for a Contrast
357
13.6.2
Orthogonal Contrasts
358
13.6.3
Orthogonal Polynomial Contrasts
363
14
Two-Way Analysis-of-Variance: Balanced Case
377
14.1
The Two-Way Model
377
14.2
Estimable Functions
378
14.3
Estimators of
λ β
and
σ2
382
14.3.1
Solving the Normal Equations and Estimating
λ β
382
14.3.2
An Estimator for
σ2
384
14.4
Testing Hypotheses
385
14.4.1
Test for Interaction
385
14.4.2
Tests for Main Effects
395
14.5
Expected Mean Squares
403
14.5.1
Sums-of-Squares Approach
403
14.5.2
Quadratic Form Approach
405
15
Analysis-of-Variance: The Cell Means Model for
Unbalanced Data
413
15.1
Introduction
413
15.2
One-Way Model
415
15.2.1
Estimation and Testing
415
15.2.2
Contrasts
417
15.3
Two-Way Model
421
15.3.1
Unconstrained Model
421
15.3.2
Constrained Model
428
15.4
Two-Way Model with Empty Cells
432
16
Analysis-of-Covariance
443
16.1
Introduction
443
16.2
Estimation and Testing
444
16.2.1
The Analysis-of-Covariance Model
444
16.2.2
Estimation
446
16.2.3
Testing Hypotheses
448
16.3
One-Way Model with One Covariate
449
16.3.1
The Model
449
16.3.2
Estimation
449
16.3.3
Testing Hypotheses
450
CONTENTS xi
16.4
Two-Way Model with One Covariate
457
16.4.1
Tests for Main Effects and Interactions
458
16.4.2
Test for Slope
462
16.4.3
Test for Homogeneity of Slopes
463
16.5
One-Way Model with Multiple Covariates
464
16.5.1
The Model
464
16.5.2
Estimation
465
16.5.3
Testing Hypotheses
468
16.6
Analysis-of-Covariance with Unbalanced Models
473
17
Linear Mixed Models
479
17.1
Introduction
479
17.2
The Linear Mixed Model
479
17.3
Examples
481
17.4
Estimation of Variance Components
486
17.5
Inference for
β
490
17.5.1
An Estimator for
β
490
17.5.2
Large-Sample Inference for Estimable Functions of
β
491
17.5.3
Small-Sample Inference for Estimable Functions of
β
491
17.6
Inference for the
ą
Terms
497
17.7
Residual Diagnostics
501
18
Additional Models
507
18.1
Nonlinear Regression
507
18.2
Logistic Regression
508
18.3 Loglinear
Models
511
18.4
Poisson
Regression
512
18.5
Generalized Linear Models
513
Appendix A Answers and Hints to the Problems
517
References
653
Index
663
|
| adam_txt |
CONTENTS
Preface
xiii
1
Introduction
1
1.1
Simple Linear Regression Model
1
1.2
Multiple Linear Regression Model
2
1.3
Analysis-of-Variance Models
3
2
Matrix Algebra
5
2.1
Matrix and Vector Notation
5
2.1.1
Matrices, Vectors, and Scalars
5
2.1.2
Matrix Equality
6
2.1.3
Transpose
7
2.1.4
Matrices of Special Form
7
2.2
Operations
9
2.2.1
Sum of Two Matrices or Two Vectors
9
2.2.2
Product of a Scalar and a Matrix
10
2.2.3
Product of Two Matrices or Two Vectors
10
2.2.4
Hadamard
Product of Two
Matrices or Two Vectors
16
2.3
Partitioned Matrices
16
2.4
Rank
19
2.5
Inverse
21
2.6
Positive Definite Matrices
24
2.7
Systems of Equations
28
2.8
Generalized Inverse
32
2.8.1
Definition and Properties
33
2.8.2
Generalized Inverses and Systems of Equations
36
2.9
Determinants
37
2.10
Orthogonal Vectors and Matrices
41
2.11
Trace
44
2.12
Eigenvalues and Eigenvectors
46
2.12.1
Definition
46
2.12.2
Functions of a Matrix
49
vi
CONTENTS
2.12.3
Products
50
2.12.4 Symmetrie
Matrices
51
2.12.5
Positive
Definite and Semidefinite
Matrices
53
2.13
Idempotent
Matrices
54
2.14
Vector and
Matrix
Calculus
56
2.14.1
Derivatives of Functions of Vectors and Matrices
56
2.14.2
Derivatives Involving Inverse Matrices and Determinants
58
2.14.3
Maximization or Minimization of a Function of a Vector
60
3
Random Vectors and Matrices
69
3.
1 Introduction
69
3.2
Means, Variances, Covariances, and Correlations
70
3.3
Mean Vectors and Covariance Matrices for Random Vectors
75
3.3.1
Mean Vectors
75
3.3.2
Covariance Matrix
75
3.3.3
Generalized Variance
77
3.3.4
Standardized Distance
77
3.4
Correlation Matrices
77
3.5
Mean Vectors and Covariance Matrices for
Partitioned Random Vectors
78
3.6
Linear Functions of Random Vectors
79
3.6.1
Means
80
3.6.2
Variances and Covariances
81
4
Multivariate Normal Distribution
87
4.1
Univariate Normal Density Function
87
4.2
Multivariate Normal Density Function
88
4.3
Moment Generating Functions
90
4.4
Properties of the Multivariate Normal Distribution
92
4.5
Partial Correlation
100
5
Distribution of Quadratic Forms in
y
105
5.1
Sums of Squares
105
5.2
Mean and Variance of Quadratic Forms
107
5.3
Noncentral Chi-Square
Distribution
112
5.4
Noncentral F
and
/
Distributions
114
5.4.1
Noncentral
Γ
Distribution
114
5.4.2
Noncentral
/
Distribution
116
5.5
Distribution of Quadratic Forms
117
5.6
Independence of Linear Forms and Quadratic Forms
119
CONTENTS
vii
6
Simple Linear Regression
127
6.1
The Model
127
6.2
Estimation of /30,
ß\,
and
σ2
128
6.3
Hypothesis Test and Confidence Interval for J3j
132
6.4
Coefficient of Determination
133
7
Multiple Regression: Estimation
137
7.1
Introduction
137
7.2
The Model
137
7.3
Estimation of
β
and
σ~
141
7.3.1
Least-Squares Estimator for
β
145
7.3.2
Properties of the Least-Squares Estimator
β
141
7.3.3
An Estimator for
cr2
149
7.4
Geometry of Least-Squares
151
7.4.1
Parameter Space, Data Space, and Prediction Space
152
7.4.2
Geometric Interpretation of the Multiple
Linear Regression Model
153
7.5
The Model in Centered Form
154
7.6
Normal Model
157
7.6.1
Assumptions
157
7.6.2
Maximum Likelihood Estimators for
β
and
σ2
158
7.6.3
Properties of
β
and
σ2
159
7.7
R2
in Fixed-x Regression
161
7.8
Generalized Least-Squares: cov(y)
=
cr'V
164
7.8.1
Estimation of
β
and
σ2
when cov(y)
=
σ2\
164
7.8.2
Misspecification of the Error Structure
167
7.9
Model Misspecification
169
7.10
Orthogonalization
174
8
Multiple Regression: Tests of Hypotheses
and Confidence Intervals
185
8.1
Test of Overall Regression
185
8.2
Test on a Subset of the
β
Values
189
8.3
F
Test in Terms of R2
196
8.4
The General Linear Hypothesis Tests for
Яо:
C/3
= 0
and Ho.
Cß =
t
198
8.4.1
The Test for
Яо:
Cß = 0 198
8.4.2
The Test for
/ƒ„:
C/3
= / 203
8.5
Tests on
β,
and a/3
204
8.5.1
Testing One
ßj
or One
aß 204
8.5.2
Testing Several
β
or a',/8 Values
205
v¡¡¡
CONTENTS
8.6
Confidence Intervals and Prediction Intervals
209
8.6.1
Confidence Region for
β
209
8.6.2
Confidence Interval for
/3, 210
8.6.3
Confidence Interval for a'
β
211
8.6.4
Confidence Interval for E(y)
211
8.6.5
Prediction Interval for a Future Observation
213
8.6.6
Confidence Interval for
σ2
215
8.6.7
Simultaneous Intervals
215
8.7
Likelihood Ratio Tests
217
9
Multiple Regression: Model Validation and Diagnostics
227
9.1
Residuals
227
9.2
The Hat Matrix
230
9.3
Outliers
232
9.4
Influential Observations and Leverage
235
10
Multiple Regression: Random jc's
243
10.1
Multivariate Normal Regression Model
244
10.2
Estimation and Testing in Multivariate Normal Regression
245
10.3
Standardized Regression Coefficents
249
10.4
R' in Multivariate Normal Regression
254
10.5
Tests and Confidence Intervals for R2
258
10.6
Effect of Each Variable on R2
262
10.7
Prediction for Multivariate Normal or
Nonnormal
Data
265
10.8
Sample Partial Correlations
266
11
Multiple Regression: Bayesian Inference
277
11.1
Elements of Bayesian Statistical Inference
277
11.2
A Bayesian Multiple Linear Regression Model
279
1
1.2.1
A Bayesian Multiple Regression Model
with a Conjugate Prior
280
11.2.2
Marginal Posterior Density of
β
282
11.2.3
Marginal Posterior Densities of
τ
and
σ2
284
11.3
Inference in Bayesian Multiple Linear Regression
285
1
1.3.1
Bayesian Point and Interval Estimates of
Regression Coefficients
285
11.3.2
Hypothesis Tests for Regression Coefficients
in Bayesian Inference
286
11.3.3
Special Cases of Inference in Bayesian Multiple
Regression Models
286
11.3.4
Bayesian Point and Interval Estimation of
σ2
287
CONTENTS
¡χ
11.4 Bayesian
Inference through Markov Chain
Monte Carlo Simulation
288
11.5
Posterior Predictive Inference
290
12
Analysis-of-Variance Models
295
12.1
Non-Full-Rank Models
295
12.1.1
One-Way Model
295
12.1.2
Two-Way Model
299
12.2
Estimation
301
12.2.1
Estimation of
β
302
12.2.2
Estimable Functions of
β
305
12.3
Estimators
309
12.3.1
Estimators of
λ'β
309
12.3.2
Estimation of
σ2
313
12.3.3
Normal Model
314
12.4
Geometry of Least-Squares in the
Overparameterized Model
316
12.5
Reparameterization
318
12.6
Side Conditions
320
12.7
Testing Hypotheses
323
12.7.1
Testable Hypotheses
323
12.7.2
Full-Reduced-Model Approach
324
12.7.3
General Linear Hypothesis
326
12.8
An Illustration of Estimation and Testing
329
12.8.1
Estimable Functions
330
12.8.2
Testing a Hypothesis
331
12.8.3
Orthogonality of Columns of X
333
13
One-Way Analysis-of-Variance: Balanced Case
339
13.1
The One-Way Model
339
13.2
Estimable Functions
340
13.3
Estimation of Parameters
341
13.3.1
Solving the Normal Equations
341
13.3.2
An Estimator for
σ2
343
13.4
Testing the Hypothesis #():
μ.\ = μ2 =
■ ■ ■ =
μ
k
344
13.4.1
Full-Reduced-Model Approach
344
13.4.2
General Linear Hypothesis
348
13.5
Expected Mean Squares
351
13.5.1
Full-Reduced-Model Approach
352
13.5.2
General Linear Hypothesis
354
x
CONTENTS
13.6
Contrasts
357
13.6.1
Hypothesis Test for a Contrast
357
13.6.2
Orthogonal Contrasts
358
13.6.3
Orthogonal Polynomial Contrasts
363
14
Two-Way Analysis-of-Variance: Balanced Case
377
14.1
The Two-Way Model
377
14.2
Estimable Functions
378
14.3
Estimators of
λ'β
and
σ2
382
14.3.1
Solving the Normal Equations and Estimating
λ'β
382
14.3.2
An Estimator for
σ2
384
14.4
Testing Hypotheses
385
14.4.1
Test for Interaction
385
14.4.2
Tests for Main Effects
395
14.5
Expected Mean Squares
403
14.5.1
Sums-of-Squares Approach
403
14.5.2
Quadratic Form Approach
405
15
Analysis-of-Variance: The Cell Means Model for
Unbalanced Data
413
15.1
Introduction
413
15.2
One-Way Model
415
15.2.1
Estimation and Testing
415
15.2.2
Contrasts
417
15.3
Two-Way Model
421
15.3.1
Unconstrained Model
421
15.3.2
Constrained Model
428
15.4
Two-Way Model with Empty Cells
432
16
Analysis-of-Covariance
443
16.1
Introduction
443
16.2
Estimation and Testing
444
16.2.1
The Analysis-of-Covariance Model
444
16.2.2
Estimation
446
16.2.3
Testing Hypotheses
448
16.3
One-Way Model with One Covariate
449
16.3.1
The Model
449
16.3.2
Estimation
449
16.3.3
Testing Hypotheses
450
CONTENTS xi
16.4
Two-Way Model with One Covariate
457
16.4.1
Tests for Main Effects and Interactions
458
16.4.2
Test for Slope
462
16.4.3
Test for Homogeneity of Slopes
463
16.5
One-Way Model with Multiple Covariates
464
16.5.1
The Model
464
16.5.2
Estimation
465
16.5.3
Testing Hypotheses
468
16.6
Analysis-of-Covariance with Unbalanced Models
473
17
Linear Mixed Models
479
17.1
Introduction
479
17.2
The Linear Mixed Model
479
17.3
Examples
481
17.4
Estimation of Variance Components
486
17.5
Inference for
β
490
17.5.1
An Estimator for
β
490
17.5.2
Large-Sample Inference for Estimable Functions of
β
491
17.5.3
Small-Sample Inference for Estimable Functions of
β
491
17.6
Inference for the
ą
Terms
497
17.7
Residual Diagnostics
501
18
Additional Models
507
18.1
Nonlinear Regression
507
18.2
Logistic Regression
508
18.3 Loglinear
Models
511
18.4
Poisson
Regression
512
18.5
Generalized Linear Models
513
Appendix A Answers and Hints to the Problems
517
References
653
Index
663 |
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| any_adam_object_boolean | 1 |
| author | Rencher, Alvin C. 1934- Schaalje, G. Bruce |
| author_GND | (DE-588)136269230 |
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| callnumber-label | QA276 |
| callnumber-raw | QA276 |
| callnumber-search | QA276 |
| callnumber-sort | QA 3276 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 230 SK 830 SK 840 |
| ctrlnum | (OCoLC)144331522 (DE-599)DNB 2007024268 |
| dewey-full | 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 |
| dewey-search | 519.5/35 |
| dewey-sort | 3519.5 235 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV022656628 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:23:35Z |
| indexdate | 2024-07-09T21:02:43Z |
| institution | BVB |
| isbn | 9780471754985 0471754986 |
| language | English |
| lccn | 2007024268 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015862554 |
| oclc_num | 144331522 |
| open_access_boolean | |
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| owner_facet | DE-355 DE-BY-UBR DE-898 DE-BY-UBR DE-20 DE-11 DE-188 |
| physical | XVI, 672 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley-Interscience |
| record_format | marc |
| spelling | Rencher, Alvin C. 1934- Verfasser (DE-588)136269230 aut Linear models in statistics Alvin C. Rencher and G. Bruce Schaalje 2. ed. Hoboken, NJ Wiley-Interscience 2008 XVI, 672 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Linear models (Statistics) Lineares Modell (DE-588)4134827-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Lineares Modell (DE-588)4134827-8 s Statistik (DE-588)4056995-0 s DE-604 Schaalje, G. Bruce Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015862554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rencher, Alvin C. 1934- Schaalje, G. Bruce Linear models in statistics Linear models (Statistics) Lineares Modell (DE-588)4134827-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4134827-8 (DE-588)4056995-0 |
| title | Linear models in statistics |
| title_auth | Linear models in statistics |
| title_exact_search | Linear models in statistics |
| title_exact_search_txtP | Linear models in statistics |
| title_full | Linear models in statistics Alvin C. Rencher and G. Bruce Schaalje |
| title_fullStr | Linear models in statistics Alvin C. Rencher and G. Bruce Schaalje |
| title_full_unstemmed | Linear models in statistics Alvin C. Rencher and G. Bruce Schaalje |
| title_short | Linear models in statistics |
| title_sort | linear models in statistics |
| topic | Linear models (Statistics) Lineares Modell (DE-588)4134827-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Linear models (Statistics) Lineares Modell Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015862554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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