Missing data:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Thousand Oaks [u.a.]
Sage
2005
|
| Edition: | [Nachdr.] |
| Series: | Quantitative applications in the social sciences
136 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | VI, 93 S. graph. Darst. |
| ISBN: | 0761916725 |
Staff View
MARC
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Record in the Search Index
| _version_ | 1804137548378800128 |
|---|---|
| adam_text | CONTENTS
Senes
Editor s Introduction
v
1.
Introduction
1
2.
Assumptions
3
Missing Completely at Random
3
Missing at Random
4
Ignorable
5
Nonignorable
5
3.
Conventional Methods
5
Listwise Deletion
6
Pairwise Deletion
8
Dummy Variable Adjustment
9
Imputation
11
Summary
12
4.
Maximum Likelihood
12
Review of Maximum Likelihood
13
ML With Missing Data
14
Contingency Table Data
15
Linear Models With Normally Distributed Data
18
The EM Algorithm
19
EM Example
21
Direct ML
23
Direct ML Example
25
Conclusion
26
5.
Multiple Imputation: Basics
27
Single Random Imputation
28
Multiple Random Imputation
29
Allowing for Random Variation in the Parameter
Estimates
30
Multiple Imputation Under the Multivariate Normal
Model
32
Data Augmentation for the Multivariate Normal Model
34
Convergence in Data Augmentation
36
Sequential Versus Parallel Chains of Data Augmentation
37
Using the Normal Model for
Nonnormal
or Categorical
Data
38
Exploratory Analysis
41
MI Example
1 41
6.
Multiple Imputation: Complications
50
Interactions and Nonlinearities in MI
50
Compatibility of the Imputation Model and the
Analysis Model
52
Role of the Dependent Variable in Imputation
53
Using Additional Variables in the Imputation Process
54
Other Parametric Approaches to Multiple Imputation
55
Nonparametric and Partially Parametric Methods
57
Sequential Generalized Regression Models
64
Linear Hypothesis Tests and Likelihood Ratio Tests
65
MI Example
2 68
MI for Longitudinal and Other Clustered Data
73
MI Example
3 74
7.
Nonignorable Missing Data
77
Two Classes of Models
78
Heckman s Model for Sample Selection Bias
79
ML Estimation With Pattern-Mixture Models
82
Multiple Imputation With Pattern-Mixture Models
83
8.
Summary and Conclusion
84
Notes
87
References
89
About the Author
93
|
| adam_txt |
CONTENTS
Senes
Editor's Introduction
v
1.
Introduction
1
2.
Assumptions
3
Missing Completely at Random
3
Missing at Random
4
Ignorable
5
Nonignorable
5
3.
Conventional Methods
5
Listwise Deletion
6
Pairwise Deletion
8
Dummy Variable Adjustment
9
Imputation
11
Summary
12
4.
Maximum Likelihood
12
Review of Maximum Likelihood
13
ML With Missing Data
14
Contingency Table Data
15
Linear Models With Normally Distributed Data
18
The EM Algorithm
19
EM Example
21
Direct ML
23
Direct ML Example
25
Conclusion
26
5.
Multiple Imputation: Basics
27
Single Random Imputation
28
Multiple Random Imputation
29
Allowing for Random Variation in the Parameter
Estimates
30
Multiple Imputation Under the Multivariate Normal
Model
32
Data Augmentation for the Multivariate Normal Model
34
Convergence in Data Augmentation
36
Sequential Versus Parallel Chains of Data Augmentation
37
Using the Normal Model for
Nonnormal
or Categorical
Data
38
Exploratory Analysis
41
MI Example
1 41
6.
Multiple Imputation: Complications
50
Interactions and Nonlinearities in MI
50
Compatibility of the Imputation Model and the
Analysis Model
52
Role of the Dependent Variable in Imputation
53
Using Additional Variables in the Imputation Process
54
Other Parametric Approaches to Multiple Imputation
55
Nonparametric and Partially Parametric Methods
57
Sequential Generalized Regression Models
64
Linear Hypothesis Tests and Likelihood Ratio Tests
65
MI Example
2 68
MI for Longitudinal and Other Clustered Data
73
MI Example
3 74
7.
Nonignorable Missing Data
77
Two Classes of Models
78
Heckman's Model for Sample Selection Bias
79
ML Estimation With Pattern-Mixture Models
82
Multiple Imputation With Pattern-Mixture Models
83
8.
Summary and Conclusion
84
Notes
87
References
89
About the Author
93 |
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| dewey-full | 001.4/22 |
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| discipline | Allgemeines Soziologie |
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| illustrated | Illustrated |
| index_date | 2024-07-02T20:26:25Z |
| indexdate | 2024-07-09T21:14:03Z |
| institution | BVB |
| isbn | 0761916725 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016432734 |
| oclc_num | 255678234 |
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| owner_facet | DE-473 DE-BY-UBG |
| physical | VI, 93 S. graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Sage |
| record_format | marc |
| series | Quantitative applications in the social sciences |
| series2 | Quantitative applications in the social sciences |
| spelling | Allison, Paul D. Verfasser (DE-588)130032336 aut Missing data Paul D. Allison [Nachdr.] Thousand Oaks [u.a.] Sage 2005 VI, 93 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Quantitative applications in the social sciences 136 Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Imputationstechnik (DE-588)4609617-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 s Statistik (DE-588)4056995-0 s DE-604 Imputationstechnik (DE-588)4609617-6 s 1\p DE-604 Quantitative applications in the social sciences 136 (DE-604)BV000005102 136 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016432734&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Allison, Paul D. Missing data Quantitative applications in the social sciences Fehlende Daten (DE-588)4264715-0 gnd Imputationstechnik (DE-588)4609617-6 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4264715-0 (DE-588)4609617-6 (DE-588)4056995-0 |
| title | Missing data |
| title_auth | Missing data |
| title_exact_search | Missing data |
| title_exact_search_txtP | Missing data |
| title_full | Missing data Paul D. Allison |
| title_fullStr | Missing data Paul D. Allison |
| title_full_unstemmed | Missing data Paul D. Allison |
| title_short | Missing data |
| title_sort | missing data |
| topic | Fehlende Daten (DE-588)4264715-0 gnd Imputationstechnik (DE-588)4609617-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Fehlende Daten Imputationstechnik Statistik |
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