Logistic regression models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2009
|
| Schriftenreihe: | Texts in statistical science
A Chapman & Hall book |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 637 S. graph. Darst. |
| ISBN: | 9781420075755 |
Internformat
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| 020 | |a 9781420075755 |9 978-1-4200-7575-5 | ||
| 035 | |a (OCoLC)226357303 | ||
| 035 | |a (DE-599)HBZHT015873382 | ||
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| 100 | 1 | |a Hilbe, Joseph M. |d 1944-2017 |e Verfasser |0 (DE-588)128751851 |4 aut | |
| 245 | 1 | 0 | |a Logistic regression models |c Joseph M. Hilbe |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XVIII, 637 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Texts in statistical science | |
| 490 | 0 | |a A Chapman & Hall book | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Logistic regression analysis |x Data processing | |
| 650 | 0 | 7 | |a Regressionsmodell |0 (DE-588)4127980-3 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804139389480075264 |
|---|---|
| adam_text | CONTENTS
PREFACE...................................................................................................................XIII
CHAPTER
1
INTRODUCTION........................................................................................1
1.1
THE
NORMAL
MODEL........................................................................................1
1.2
FOUNDATION
OF
THE
BINOMIAL
MODEL.............................................................1
1.3
HISTORICAL
AND
SOFTWARE
CONSIDERATIONS....................................................3
1.4
CHAPTER
PROFILES............................................................................................10
CHAPTER
2
CONCEPTS
RELATED
TO
THE
LOGISTIC
MODEL......................................15
2.1
2
X
2
TABLE
LOGISTIC
MODEL..........................................................................16
2.2
2X1
TABLE
LOGISTIC
MODEL...........................................................................25
2.3
MODELING
A
QUANTITATIVE
PREDICTOR...........................................................38
2.4
LOGISTIC
MODELING
DESIGNS..............
42
2.4.1
EXPERIMENTAL
STUDIES......................................................................43
2.4.2
OBSERVATIONAL
STUDIES.....................................................................43
2.4.2.1
PROSPECTIVE
OR
COHORT
STUDIES.........................................43
2.4.2.2
RETROSPECTIVE
OR
CASE-CONTROL
STUDIES..........................44
2.4.2.3
COMPARISONS......................................................................44
EXERCISES.................................................................................................................45
R
CODE....................................................................................................................47
CHAPTER
3
ESTIMATION
METHODS........................................................................51
3.1
DERIVATION
OF
THE
IRLS
ALGORITHM.............................................................51
3.2
IRLS
ESTIMATION............................................................................................56
3.3
MAXIMUM
LIKELIHOOD
ESTIMATION.............................................................58
EXERCISES.................................................................................................................61
R
CODE....................................................................................................................62
CHAPTER
4
DERIVATION
OF
THE
BINARY
LOGISTIC
ALGORITHM.............................63
4.1
TERMS
OF
THE
ALGORITHM...............................................................................63
4.2
LOGISTIC
GLM
AND
ML
ALGORITHMS...........................................................67
4.3
OTHER
BERNOULLI
MODELS..............................................................................68
EXERCISES.................
70
R
CODE....................................................................................................................71
CHAPTER
5
MODEL
DEVELOPMENT........................................................................73
5.1
BUILDING
A
LOGISTIC
MODEL..........................................................................73
5.1.1
INTERPRETATIONS..................................................................................76
5.1.2
FULL
MODEL.........................................................................................79
5.1.3
REDUCED
MODEL................................................................................81
VII
VIII
CONTENTS
5.2
ASSESSING
MODEL
FIT:
LINK
SPECIFICATION...................................................82
5.2.1
BOX-TIDWELL
TEST...............................................................................83
5.2.2
TUKEY-PREGIBON
LINK
TEST..............................................................84
5.2.3
TEST
BY
PARTIAL
RESIDUALS.................................................................85
5.2.4
LINEARITY
OF
SLOPES
TEST...................................................................87
5.2.5
GENERALIZED
ADDITIVE
MODELS........................................................90
5.2.6
FRACTIONAL
POLYNOMIALS....................................................................95
5.3
STANDARDIZED
COEFFICIENTS..........................................................................99
5.4
STANDARD
ERRORS..........................................................................................102
5.4.1
CALCULATING
STANDARD
ERRORS........................................................102
5.4.2
THE
Z-STATISTIC..................................................................................103
5.4.3
P-VALUES............................................................................................104
5.4.4
CONFIDENCE
INTERVALS......................................................................104
5.4.5
CONFIDENCE
INTERVALS
OF
ODDS
RATIOS..........................................106
5.5
ODDS
RATIOS
AS
APPROXIMATIONS
OF
RISK
RATIOS....................................106
5.5.1
EPIDEMIOLOGICAL
TERMS
AND
STUDIES............................................106
5.5.2
ODDS
RATIOS,
RISK
RATIOS,
AND
RISK
MODELS...............................109
5.5.3
CALCULATING
STANDARD
ERRORS
AND
CONFIDENCE
INTERVALS...........................................................................................121
5.5.4
RISK
DIFFERENCE
AND
ATTRIBUTABLE
RISK........................................127
5.5.5
OTHER
RESOURCES
ON
ODDS
RATIOS
AND
RISK
RATIOS......................................................................................131
5.6
SCALING
OF
STANDARD
ERRORS.......................................................................132
5.7
ROBUST
VARIANCE
ESTIMATORS.....................................................................136
5.8
BOOTSTRAPPED
AND
JACKKNIFED
STANDARD
ERRORS.....................................139
5.9
STEPWISE
METHODS......................................................................................143
5.10
HANDLING
MISSING
VALUES.........................................................................148
5.11
MODELING
AN
UNCERTAIN
RESPONSE...........................................................158
5.12
CONSTRAINING
COEFFICIENTS.........................................................................161
EXERCISES...............................................................................................................165
RCODE..................................................................................................................171
CHAPTER
6
INTERACTIONS......................................................................................189
6.1
INTRODUCTION................................................................................................189
6.2
BINARY
X
BINARY
INTERACTIONS....................................................................191
6.2.1
INTERPRETATION*AS
ODDS
RATIO.....................................................194
6.2.2
STANDARD
ERRORS
AND
CONFIDENCE
INTERVALS................................197
6.2.3
GRAPHICAL
ANALYSIS........................................................................198
6.3
BINARY
X
CATEGORICAL
INTERACTIONS............................................................201
6.4
BINARY
X
CONTINUOUS
INTERACTIONS...........................................................206
6.4.1
NOTES
ON
CENTERING........................................................................206
6.4.2
CONSTRUCTING
AND
INTERPRETING
THE
INTERACTION..........................209
6.4.3
INTERPRETATION..................................................................................213
6.4.4
STANDARD
ERRORS
AND
CONFIDENCE
INTERVALS................................215
CONTENTS
IX
6.4.5
SIGNIFICANCE
OF
INTERACTION............................................................217
6.4.6
GRAPHICAL
ANALYSIS........................................................................217
6.5
CATEGORICAL
X
CONTINUOUS
INTERACTIONS...................................................221
6.5.1
INTERPRETATION..................................................................................223
6.5.2
STANDARD
ERRORS
AND
CONFIDENCE
INTERVALS................................225
6.5.3
GRAPHICAL
REPRESENTATION.............................................................225
6.6
THOUGHTS
ABOUT
INTERACTIONS....................................................................228
6.6.1
BINARY
X
BINARY..............................................................................230
6.6.2
CONTINUOUS
X
BINARY.....................................................................230
6.6.3
CONTINUOUS
X
CONTINUOUS.............................................................230
EXERCISES...............................................................................................................233
R
CODE..................................................................................................................235
CHAPTER
7
ANALYSIS
OF
MODEL
FIT....................................................................243
7.1
TRADITIONAL
FIT
TESTS
FOR
LOGISTIC
REGRESSION..........................................243
7.1.1
R
2
AND
PSEUDO-R
2
STATISTICS.....................................
243
7.1.2
DEVIANCE
STATISTIC...........................................................................246
7.1.3
LIKELIHOOD
RATIO
TEST....................................................................248
7.2
HOSMER-LEMESHOW
GOF
TEST.................................................................249
7.2.1
HOSMER-LEMESHOW
GOF
TEST.....................................................250
7.2.2
CLASSIFICATION
MATRIX......................................................................254
7.2.3
ROC
ANALYSIS.................................................................................258
7.3
INFORMATION
CRITERIA
TESTS.........................................................................259
7.3.1
AKAIKE
INFORMATION
CRITERION*AIC...........................................259
7.3.2
FINITE
SAMPLE
AIC
STATISTIC...........................................................262
7.3.3
LIMDEP
AIC...................................................................................263
7.3.4
SWARTZ
AIC..................................................................................263
7.3.5
BAYESIAN
INFORMATION
CRITERION
(BIC).........................................263
7.3.6
HQIC
GOODNESS-OF-FIT
STATISTIC....................................................267
7.3.7
A
UNIFIED
AIC
FIT
STATISTIC............................................................267
7.4
RESIDUAL
ANALYSIS......................................................................................268
7.4.1
GLM-BASED
RESIDUALS...................................................................269
7.4.1.1
RAW
RESIDUAL...................................................................270
7.4.1.2
PEARSON
RESIDUAL.............................................................271
7.4.1.3
DEVIANCE
RESIDUAL...........................................................272
7.4.1.4
STANDARDIZED
PEARSON
RESIDUAL....................................274
7.4.1.5
STANDARDIZED
DEVIANCE
RESIDUAL..................................276
7.4.1.6
LIKELIHOOD
RESIDUALS......................................................279
7.4.1.7
ANSCOMBE
RESIDUALS......................................................279
7.4.2
M-ASYMPTOTIC
RESIDUALS...............................................................280
7.4.2.1
HAT
MATRIX
DIAGONAL
REVISITED.....................................281
7.4.2.2
OTHER
INFLUENCE
RESIDUALS..............................................281
7.4.3
CONDITIONAL
EFFECTS
PLOT................................................................284
7.5
VALIDATION
MODELS......................................................................................286
X
CONTENTS
9
EXERCISES...............................................................................................................289
R
CODE..................................................................................................................291
CHAPTER
8
BINOMIAL
LOGISTIC
REGRESSION......................................................297
EXERCISES...............................................................................................................313
RCODE..................................................................................................................316
CHAPTER
9
OVERDISPERSION...............................................................................319
9.1
INTRODUCTION.....................
319
9.2
THE
NATURE
AND
SCOPE
OF
OVERDISPERSION..............................................319
9.3
BINOMIAL
OVERDISPERSION..........................................................................320
9.3.1
APPARENT
OVERDISPERSION.............................................................321
9.3.1.1
SIMULATED
MODEL
SETUP..................................................322
9.3.1.2
MISSING
PREDICTOR............................................................323
9.3.1.3
NEEDED
INTERACTION.........................................................324
9.3.1.4
PREDICTOR
TRANSFORMATION...............................................326
9.3.1.5
MISSPECIFIED
LINK
FUNCTION...........................................327
9.3.1.6
EXISTING
OUTLIER(S)...........................................................329
9.3.2
RELATIONSHIP:
BINOMIAL
AND
POISSON............................................334
9.4
BINARY
OVERDISPERSION..............................................................................338
9.4.1
THE
MEANING
OF
BINARY
MODEL
OVERDISPERSION........................338
9.4.2
IMPLICIT
OVERDISPERSION................................................................341
9.5
REAL
OVERDISPERSION..................................................................................341
9.5.1
METHODS
OF
HANDLING
REAL
OVERDISPERSION...............................341
9.5.2
WILLIAMS PROCEDURE......................................................................342
9.5.3
GENERALIZED
BINOMIAL
REGRESSION...............................................345
9.6
CONCLUDING
REMARKS.................................................................................346
EXERCISES...............................................................................................................346
RCODE..................................................................................................................348
CHAPTER
10
ORDERED
LOGISTIC
REGRESSION.....................................................353
10.1
INTRODUCTION................................................................................................353
10.2
THE
PROPORTIONAL
ODDS
MODEL.................................................................355
10.3
GENERALIZED
ORDINAL
LOGISTIC
REGRESSION..............................................375
10.4
PARTIAL
PROPORTIONAL
ODDS........................................................................376
EXERCISES...............................................................................................................378
R
CODE..................................................................................................................381
CHAPTER
11
MULTINOMIAL
LOGISTIC
REGRESSION.............................................385
11.1
UNORDERED
LOGISTIC
REGRESSION................................................................385
11.1.1
THE
MULTINOMIAL
DISTRIBUTION.....................................................385
11.1.2
INTERPRETATION
OF
THE
MULTINOMIAL
MODEL...................................387
11.2
INDEPENDENCE
OF
IRRELEVANT
ALTERNATIVES................................................396
11.3
COMPARISON
TO
MULTINOMIAL
PROBIT.........................................................399
CONTENTS
XI
EXERCISES...............................................................................................................405
R
CODE..................................................................................................................407
CHAPTER
12
ALTERNATIVE
CATEGORICAL
RESPONSE
MODELS.............................
411
12.1
INTRODUCTION................................................................................................411
12.2
CONTINUATION
RATIO
MODELS......................................................................412
12.3
STEREOTYPE
LOGISTIC
MODEL........................................................................419
12.4
HETEROGENEOUS
CHOICE
LOGISTIC
MODEL...................................................422
12.5
ADJACENT
CATEGORY
LOGISTIC
MODEL..........................................................427
12.6
PROPORTIONAL
SLOPES
MODELS.....................................................................429
12.6.1
PROPORTIONAL
SLOPES
COMPARATIVE
ALGORITHMS..........................430
12.6.2
MODELING
SYNTHETIC
DATA..............................................................432
12.6.3
TESTS
OF
PROPORTIONALITY.................................................................435
EXERCISES;..............................................................................................................438
CHAPTER
13
PANEL
MODELS.................................................................................
441
13.1
INTRODUCTION................................................................................................441
13.2
GENERALIZED
ESTIMATING
EQUATIONS
;........................................................442
13.2.1
GEE:
OVERVIEW
OF
GEE
THEORY....................................................444
13.2.2
GEE
CORRELATION
STRUCTURES..........................................................446
13.2.2.1
INDEPENDENCE
CORRELATION
STRUCTURE
SCHEMATIC.........448
13.2.2.2
EXCHANGEABLE
CORRELATION
STRUCTURE
SCHEMATIC.........450
13.2.2.3
AUTOREGRESSIVE
CORRELATION
STRUCTURE
SCHEMATIC.......451
13.2.2.4
UNSTRUCTURED
CORRELATION
STRUCTURE
SCHEMATIC.........453
13.2.2.5
STATIONARY
OR
M-DEPENDENT
CORRELATION
STRUCTURE
SCHEMATIC..........................................................................455
13.2.2.6
NONSTATIONARY
CORRELATION
STRUCTURE
SCHEMATIC........456
13.2.3
GEE
BINOMIAL
LOGISTIC
MODELS....................................................458
13.2.4
GEE
FIT
ANALYSIS*QIC.................................................................460
13.2.4.1
QIC/QICU
SUMMARY-BINARY
LOGISTIC
REGRESSION
....464
13.2.5
ALTERNATING
LOGISTIC
REGRESSION..................................................466
13.2.6
QUASI-LEAST
SQUARES
REGRESSION..................................................470
13.2.7
FEASIBILITY........................................................................................474
13.2.8
FINAL
COMMENTS
ON
GEE...............................................................479
13.3
UNCONDITIONAL
FIXED
EFFECTS
LOGISTIC
MODEL..........................................481
13.4
CONDITIONAL
LOGISTIC
MODELS....................................................................483
13.4.1
CONDITIONAL
FIXED
EFFECTS
LOGISTIC
MODELS................................483
13.4.2
MATCHED
CASE-CONTROL
LOGISTIC
MODEL.......................................487
13.4.3
RANK-ORDERED
LOGISTIC
REGRESSION.............................................490
13.5
RANDOM
EFFECTS
AND
MIXED
MODELS
LOGISTIC
REGRESSION....................496
13.5.1
RANDOM
EFFECTS
AND
MIXED
MODELS:
BINARY
RESPONSE............496
13.5.2
ALTERNATIVE
AIC-TYPE
STATISTICS
FOR
PANEL
DATA.........................504
13.5.3
RANDOM-INTERCEPT
PROPORTIONAL
ODDS........................................505
EXERCISES...............................................................................................................510
R
CODE..................................................................................................................514
CONTENTS
XII
CHAPTER
14
OTHER
TYPES
OF
LOGISTIC-BASED
MODELS....................................519
14.1
SURVEY
LOGISTIC
MODELS.............................................................................519
14.1.1
INTERPRETATION..................................................................................524
14.2
SCOBIT-SKEWED
LOGISTIC
REGRESSION.........................................................528
14.3
DISCRIMINANT
ANALYSIS..............................................................................531
14.3.1
DICHOTOMOUS
DISCRIMINANT
ANALYSIS.........................................532
14.3.2
CANONICAL
LINEAR
DISCRIMINANT
ANALYSIS...................................536
14.3.3
LINEAR
LOGISTIC
DISCRIMINANT
ANALYSIS......................................539
EXERCISES...................................;...........................................................................540
CHAPTER
15
EXACT
LOGISTIC
REGRESSION...........................................................543
15.1
EXACT
METHODS............................................................................................543
15.2
ALTERNATIVE
MODELING
METHODS...............................................................550
15.2.1
MONTE
CARLO
SAMPLING
METHODS..................................................550
15.2.2
MEDIAN
UNBIASED
ESTIMATION......................................................552
15.2.3
PENALIZED
LOGISTIC
REGRESSION.....................................................554
EXERCISES...............................................................................................................558
CONCLUSION...........................................................................................................559
APPENDIX
A:
BRIEF
GUIDE
TO
USING
STATA
COMMANDS.................................561
APPENDIX
B:
STATA
AND
R
LOGISTIC
MODELS....................................................589
APPENDIX
C:
GREEK
LETTERS
AND
MAJOR
FUNCTIONS.......................................591
APPENDIX
D:
STATA
BINARY
LOGISTIC
COMMAND............................................593
APPENDIX
E:
DERIVATION
OF
THE
BETA
BINOMIAL.............................................597
APPENDIX
F:
LIKELIHOOD
FUNCTION
OF
THE
ADAPTIVE
GAUSS-HERMITE
QUADRATURE
METHOD
OF
ESTIMATION.................................................................599
APPENDIX
G:
DATA
SETS.....................................................................................601
APPENDIX
H:
MARGINAL
EFFECTS
AND
DISCRETE
CHANGE................................605
REFERENCES...........................................................................................................613
AUTHOR
INDEX.......................................................................................................625
SUBJECT
INDEX......................................................................................................629
|
| any_adam_object | 1 |
| author | Hilbe, Joseph M. 1944-2017 |
| author_GND | (DE-588)128751851 |
| author_facet | Hilbe, Joseph M. 1944-2017 |
| author_role | aut |
| author_sort | Hilbe, Joseph M. 1944-2017 |
| author_variant | j m h jm jmh |
| building | Verbundindex |
| bvnumber | BV035682250 |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278.2 |
| callnumber-search | QA278.2 |
| callnumber-sort | QA 3278.2 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 840 |
| classification_tum | MAT 628f |
| ctrlnum | (OCoLC)226357303 (DE-599)HBZHT015873382 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV035682250 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:43:18Z |
| institution | BVB |
| isbn | 9781420075755 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017736503 |
| oclc_num | 226357303 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-1028 DE-473 DE-BY-UBG DE-522 DE-824 DE-188 DE-19 DE-BY-UBM |
| owner_facet | DE-91G DE-BY-TUM DE-1028 DE-473 DE-BY-UBG DE-522 DE-824 DE-188 DE-19 DE-BY-UBM |
| physical | XVIII, 637 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Texts in statistical science A Chapman & Hall book |
| spelling | Hilbe, Joseph M. 1944-2017 Verfasser (DE-588)128751851 aut Logistic regression models Joseph M. Hilbe Boca Raton [u.a.] CRC Press 2009 XVIII, 637 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Texts in statistical science A Chapman & Hall book Datenverarbeitung Logistic regression analysis Data processing Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Logistische Verteilung (DE-588)4299453-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Logistische Verteilung (DE-588)4299453-6 s DE-604 Regressionsmodell (DE-588)4127980-3 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736503&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hilbe, Joseph M. 1944-2017 Logistic regression models Datenverarbeitung Logistic regression analysis Data processing Regressionsmodell (DE-588)4127980-3 gnd Logistische Verteilung (DE-588)4299453-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4127980-3 (DE-588)4299453-6 (DE-588)4129903-6 |
| title | Logistic regression models |
| title_auth | Logistic regression models |
| title_exact_search | Logistic regression models |
| title_full | Logistic regression models Joseph M. Hilbe |
| title_fullStr | Logistic regression models Joseph M. Hilbe |
| title_full_unstemmed | Logistic regression models Joseph M. Hilbe |
| title_short | Logistic regression models |
| title_sort | logistic regression models |
| topic | Datenverarbeitung Logistic regression analysis Data processing Regressionsmodell (DE-588)4127980-3 gnd Logistische Verteilung (DE-588)4299453-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Datenverarbeitung Logistic regression analysis Data processing Regressionsmodell Logistische Verteilung Regressionsanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736503&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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