Interpreting standard and nonstandard log-linear models:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Münster ; München [u.a.]
Waxmann
2006
|
| Schriftenreihe: | Internationale Hochschulschriften
455 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014767242&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | 164 S. graph. Darst. |
| ISBN: | 3830916116 |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV021551177 | ||
| 003 | DE-604 | ||
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| 100 | 1 | |a Mair, Patrick |e Verfasser |0 (DE-588)131503707 |4 aut | |
| 245 | 1 | 0 | |a Interpreting standard and nonstandard log-linear models |c Patrick Mair |
| 264 | 1 | |a Münster ; München [u.a.] |b Waxmann |c 2006 | |
| 300 | |a 164 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Internationale Hochschulschriften |v 455 | |
| 650 | 4 | |a Log-linear models | |
| 650 | 0 | 7 | |a Log-lineares Modell |0 (DE-588)4036197-4 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Log-lineares Modell |0 (DE-588)4036197-4 |D s |
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| 830 | 0 | |a Internationale Hochschulschriften |v 455 |w (DE-604)BV035417764 |9 455 | |
| 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=014767242&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-014767242 | |
Datensatz im Suchindex
| _version_ | 1819269507532718080 |
|---|---|
| adam_text | Contents
1
Introduction
..............................................................................................9
2
Analysis of Contingency Tables
............................................................11
2.1
Two-way Tables
.....................................................................................11
2.1.1
Survey Methods in Two-way Tables
.....................................................12
2.1.2
Hypotheses and Testing in Two-way Tables
.........................................15
2.2
Odds Ratios
............................................................................................17
3
Hierarchical Log-Linear Models
..............................................................23
3.1
The Derivation of the Log-Linear Model
................................................23
3.2
Interpretation of Single Parameters in Log-Linear Models
.....................25
3.2.1
Saturated Model and Restrictions
..........................................................25
3.2.2
Interpretation of the Parameters
.............................................................27
3.2.3
Estimation of the Parameters
..................................................................30
3.3
Interpreting Hierarchical Log-Linear Models
........................................30
3.3.1
Hierarchy principle
.................................................................................30
3.3.2
Interpretation of Reduced Models
..........................................................33
3.4
Testing and Model Selection
..................................................................37
3.4.1
Testing for Goodness of Fit
....................................................................37
3.4.2
Model Selection Criteria
........................................................................39
3.4.3
Testing Single Parameters
......................................................................41
3.5
Concluding Example and Further Tasks
................................................42
3.5.1
Example for Model Testing
....................................................................42
3.5.2
Further Tasks in Log-Linear Modeling
..................................................46
4
Generalized Linear Models
....................................................................49
4.1
Introduction and Definitions
..................................................................49
4.2
General Linear Models
...........................................................................51
4.2.1
Definition of the General Linear Model
.................................................51
4.2.2
The Multiple Regression Model
.............................................................52
4.2.3
The Analysis of Variance
.......................................................................53
5
Log-Linear Models in the Context of GLM
...........................................55
5.1
Coding Strategies for Design Matrices
..................................................55
5.1.1
Dummy Coding
......................................................................................56
5.1.2
Unweighted Effect Coding
.....................................................................58
5.1.3
Weighted Effect Coding
.........................................................................59
5.1.4
Orthogonal Coding
.................................................................................59
5.1.5
Nonsense Coding
....................................................................................61
5.1.6
A Computation Procedure for Design Matrices
.....................................61
5.2
Issues of Orthogonality in Design Matrices
...........................................62
5.2.1
Multicollinearity in Regression Models
.................................................62
5.2.2
Orthogonal vs.
Nonorthogonal
Design Vectors
.....................................63
5.2.3
The Phenomenon of Block Orthogonality
.............................................65
5.2.4
Further Aspects of Correlation between Design Vectors
.......................67
5.3
GLM Implementation of Log-Linear Models
........................................69
5.3.1
The Basic GLM Structure of the Log-Linear Representation
................69
5.3.2
Design Matrix Strategies for Log-Linear Modeling
..............................70
5.4
Justification of a New Approach for Parameter Contribution
-
Part
1.. 73
6
Nonstandard
Log-Linear Models
...........................................................75
6.1
Limitations of Nonhierarchical Log-Linear Models
..............................75
6.1.1
Definition of Nonhierarchical Models
...................................................75
6.1.2
Discussions about the Legitimation of Nonhierarchical Models
...........76
6.2
Nonstandard
Models as GLM
................................................................78
6.2.1
Some Introductory Definitions
...............................................................78
6.2.2
The Usefulness of
Nonstandard
Models
................................................80
6.3
Theoretical Funding vs.
Data-Driven
Approach for Parameter
Interpretation
..........................................................................................83
6.4
Justification of a New Approach for Parameter Contribution
-
Part
2.. 84
7
Stepwise Approaches for Model Selection
............................................85
7.1
Ordinary Stepwise Regression
...............................................................85
7.1.1
Criteria for Model Selection Based on Sum of Squares
DecompositionSo
7.1.2
Stepwise Regression, Forward and Backward Elimination
...................87
7.2
Stepwise Procedures for Log-Linear Model Selection
..........................88
7.2.1
Stepwise Selection for a Given Model Hierarchy
..................................89
7.2.2
Screening Effects Procedure
..................................................................90
7.2.3
Goodman s Stepwise Procedure
.............................................................91
7.2.4
Model Selection According to Edwards
&
Havránek
............................92
7.3
Justification of a New Approach for Parameter Contribution
-
Part
3.. 93
8
A Procedure for Analyzing Standard and
Nonstandard
Models
............95
8.1
Entering of the Model Effects
................................................................96
8.1.1
Correlation between Design Vectors and Dependent Variables
in Ordinary Regression
...........................................................................96
8.1.2
Coefficients of Partial Correlation and Determination
in Ordinary Regression
...........................................................................98
8.1.3
Implications of Correlation Analysis in Log-Linear Models
.................99
8.1.4
Implications of Partial Correlations in Log-Linear Models
.................102
8.2
Adopting Goodman s Forward Selection
.............................................104
8.3
Relative Importance of Single Parameters
...........................................105
8.3.1
Relative Importance by Averaging
Orderings
.....................................105
8.3.2
An Approach Based on Information Theory
........................................106
8.3.3
The Dominance Analysis
.....................................................................107
8.4
Some Pseudo-R2 Measures
...................................................................110
8.4.1
The Standard Approach for Pseudo-R2 in Log-Linear Models
............110
8.4.2
Further Proposals of Pseudo-R2 Measures
...........................................111
8.5
Concluding Example
............................................................................112
9
Testing Non-Nested Models
.................................................................115
9.1
General Remarks for Model Testing
....................................................115
9.2
A Likelihood Ratio Test for Non-Nested Models
................................116
9.2.1
General Definitions and Assumptions
..................................................116
9.2.2
Implications for Non-Nested and Overlapping Models
.......................119
9.3
Comparing Two AIC s
.........................................................................121
9.3.1
Introductory Topics
..............................................................................121
9.3.2
The Use of AIC in Log-Linear Models
................................................122
9.3.3
A Test whether Two AIC s Differ Significantly
..................................123
10
Algorithmic Implementation
................................................................127
10.1
General Annotation
..............................................................................127
10.2
Components of the New Procedure
......................................................127
10.2.1
Input and Output
...................................................................................127
10.2.2
Determining the Routine
......................................................................129
11
Examples
..............................................................................................133
11.1
Standard Log-Linear Model
.................................................................133
11.2
Nonstandard
Model with Special Coding Vector
................................139
11.3
Example for Linhart s Test Procedure
.................................................142
11.4
The Weak Wedge
.................................................................................145
12
Summary
...............................................................................................149
13
Discussion
.............................................................................................151
References
.........................................................................................................155
|
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV021551177 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T12:36:15Z |
| institution | BVB |
| isbn | 3830916116 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014767242 |
| oclc_num | 69983430 |
| open_access_boolean | |
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| owner_facet | DE-19 DE-BY-UBM DE-12 DE-355 DE-BY-UBR DE-634 |
| physical | 164 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Waxmann |
| record_format | marc |
| series | Internationale Hochschulschriften |
| series2 | Internationale Hochschulschriften |
| spellingShingle | Mair, Patrick Interpreting standard and nonstandard log-linear models Internationale Hochschulschriften Log-linear models Log-lineares Modell (DE-588)4036197-4 gnd |
| subject_GND | (DE-588)4036197-4 (DE-588)4113937-9 |
| title | Interpreting standard and nonstandard log-linear models |
| title_auth | Interpreting standard and nonstandard log-linear models |
| title_exact_search | Interpreting standard and nonstandard log-linear models |
| title_full | Interpreting standard and nonstandard log-linear models Patrick Mair |
| title_fullStr | Interpreting standard and nonstandard log-linear models Patrick Mair |
| title_full_unstemmed | Interpreting standard and nonstandard log-linear models Patrick Mair |
| title_short | Interpreting standard and nonstandard log-linear models |
| title_sort | interpreting standard and nonstandard log linear models |
| topic | Log-linear models Log-lineares Modell (DE-588)4036197-4 gnd |
| topic_facet | Log-linear models Log-lineares Modell Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014767242&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035417764 |
| work_keys_str_mv | AT mairpatrick interpretingstandardandnonstandardloglinearmodels |