Maximum likelihood estimation with Stata:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press [u.a.]
2010
|
| Ausgabe: | 4. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 352 S. graph. Darst. |
| ISBN: | 1597180785 9781597180788 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV037331669 | ||
| 003 | DE-604 | ||
| 005 | 20141222 | ||
| 007 | t | ||
| 008 | 110411s2010 d||| |||| 00||| eng d | ||
| 020 | |a 1597180785 |9 1-59718-078-5 | ||
| 020 | |a 9781597180788 |9 978-1-59718-078-8 | ||
| 035 | |a (OCoLC)700334195 | ||
| 035 | |a (DE-599)HBZHT016683735 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-188 |a DE-M382 |a DE-473 |a DE-19 |a DE-703 |a DE-20 |a DE-N2 |a DE-384 | ||
| 084 | |a MR 2100 |0 (DE-625)123488: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Gould, William |d 1952- |e Verfasser |0 (DE-588)1059407698 |4 aut | |
| 245 | 1 | 0 | |a Maximum likelihood estimation with Stata |c William Gould ; Jeffrey Pitblado ; Brian Poi |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a College Station, Tex. |b Stata Press [u.a.] |c 2010 | |
| 300 | |a XXII, 352 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Statistische Analyse |0 (DE-588)4116599-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maximum-Likelihood-Schätzung |0 (DE-588)4194624-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Stata |0 (DE-588)4617285-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Statistische Analyse |0 (DE-588)4116599-8 |D s |
| 689 | 0 | 1 | |a Stata |0 (DE-588)4617285-3 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Maximum-Likelihood-Schätzung |0 (DE-588)4194624-8 |D s |
| 689 | 1 | 1 | |a Stata |0 (DE-588)4617285-3 |D s |
| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Pitblado, Jeffrey |e Verfasser |4 aut | |
| 700 | 1 | |a Poi, Brian |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022485561&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-022485561 | ||
Datensatz im Suchindex
| _version_ | 1804145615308849152 |
|---|---|
| adam_text | Contents
List of tables
xiii
List of figures
xv
Preface to the fourth edition
xvii
Versions of
Stata
xix
Notation and typography
xxi
Theory and practice
1
1.1
The likelihood-maximization problem
.................. 2
1.2
Likelihood theory
............................. 4
1.2.1
All results are asymptotic
................... 8
1.2.2
Likelihood-ratio tests and
Wald
tests
............. 9
1.2.3
The outer product of gradients variance estimator
...... 10
1.2.4
Robust variance estimates
................... 11
1.3
The maximization problem
....................... 13
1.3.1
Numerical root finding
..................... 13
Newton s method
........................ 13
The Newton-Raphson algorithm
............... 15
1.3.2
Quasi-Newton
methods
..................... 17
The BHHH algorithm
..................... 18
The DFP and BFGS algorithms
................ 18
1.3.3
Numerical maximization
.................... 19
1.3.4
Numerical derivatives
...................... 20
1.3.5
Numerical second derivatives
................. 24
1.4
Monitoring convergence
......................... 25
vi
Contents
2
Introduction
to ml
29
2.1
The
probit
model
............................. 29
2.2
Normal linear regression
......................... 32
2.3
Robust standard errors
.......................... 34
2.4
Weighted estimation
........................... 35
2.5
Other features of method-gfO evaluators
................ 36
2.6
Limitations
................................ 36
3
Overview of ml
39
3.1
The terminology of ml
.......................... 39
3.2
Equations in ml
.............................. 40
3.3
Likelihood-evaluator methods
...................... 48
3.4
Tools for the ml programmer
...................... 51
3.5
Common ml options
........................... 51
3.5.1
Subsamples
........................... 51
3.5.2
Weights
............................. 52
3.5.3
OPG estimates of variance
................... 53
3.5.4
Robust estimates of variance
.................. 54
3.5.5
Survey data
........................... 56
3.5.6
Constraints
........................... 57
3.5.7
Choosing among the optimization algorithms
........ 57
3.6
Maximizing your own likelihood functions
............... 61
4
Method If
63
4.1
The linear-form restrictions
....................... 64
4.2
Examples
................................. 65
4.2.1
The
probit
model
........................ 65
4.2.2
Normal linear regression
.................... 66
4.2.3
The Weibull model
....................... 69
4.3
The importance of generating temporary variables as doubles
.... 71
4.4
Problems you can safely ignore
..................... 73
Contents
vii
4.5
Nonlinear specifications
......................... 74
4.6
The advantages of If in terms of execution speed
........... 75
5
Methods
1ГО,
lfl, and If2
77
5.1
Comparing these methods
........................ 77
5.2
Outline of evaluators of methods
1Ю,
lfl, and If2
............ 78
5.2.1
The
todo
argument
....................... 79
5.2.2
The
b
argument
......................... 79
Using mleval to obtain values from each equation
...... 80
5.2.3
The lnfj argument
........................ 82
5.2.4
Arguments for scores
...................... 83
5.2.5
The
H
argument
........................ 84
Using mlmatsum to define
H
.................. 86
5.2.6
Aside: Stata s scalars
...................... 87
5.3
Summary of methods
1Ю,
lfl, and If2
.................. 90
5.3.1
Method lfO
............................ 90
5.3.2
Method lfl
............................ 92
5.3.3
Method If2
............................ 94
5.4
Examples
................................. 96
5.4.1
The
probit
model
........................ 96
5.4.2
Normal linear regression
.................... 98
5.4.3
The Weibull model
....................... 104
6
Methods dO, dl, and d2
109
6.1
Comparing these methods
........................ 109
6.2
Outline of method dO, dl, and d2 evaluators
.............. 110
6.2.1
The
todo
argument
.......................
Ill
6.2.2
The
b
argument
.........................
Ill
6.2.3
The lnf argument
........................ 112
Using lnf to indicate that the likelihood cannot be calculated
113
Using mlsum to
denne
lnf
................... 114
viii Contents
6.2.4
The
g
argument
......................... 116
Using mlvecsum to define
g
.................. 116
6.2.5
The
H
argument
........................ 118
6.3
Summary of methods dO, dl, and d2
.................. 119
6.3.1
Method dO
............................ 119
6.3.2
Method dl
............................ 122
6.3.3
Method d2
............................ 124
6.4
Panel-data likelihoods
.......................... 126
6.4.1
Calculating lnf
......................... 128
6.4.2
Calculating
g
.......................... 132
6.4.3
Calculating
H
.......................... 136
Using mlmatbysum to help define
H
............. 136
6.5
Other models that do not meet the linear-form restrictions
...... 144
7
Debugging likelihood evaluators
151
7.1
ml check
.................................. 151
7.2
Using the debug methods
........................ 153
7.2.1
First derivatives
......................... 155
7.2.2
Second derivatives
....................... 165
7.3
ml trace
.................................. 168
8
Setting initial values
171
8.1
ml search
................................. 172
8.2
ml plot
................................... 175
8.3
ml init
................................... 177
9
Interactive maximization
181
9.1
The iteration log
............................. 181
9.2
Pressing the Break key
.......................... 182
9.3
Maximizing difficult likelihood functions
................ 184
10
Final results
187
10.1
Graphing convergence
.......................... 187
10.2
Redisplaying output
........................... 188
Contents ix
11 Mata-based
likelihood evaluators
193
11.1
Introductory examples
.......................... 193
11.1.1 The
probit
model
........................ 193
11.1.2
The Weibull model
....................... 196
11.2
Evaluator
function prototypes
...................... 198
Method-lf evaluators
...................... 199
lf-family evaluators
....................... 199
d-family evaluators
....................... 200
11.3
Utilities
.................................. 201
Dependent variables
...................... 202
Obtaining model parameters
.................. 202
Summing individual or group-level log likelihoods
...... 203
Calculating the gradient vector
................ 203
Calculating the Hessian
.................... 204
11.4
Random-effects linear regression
..................... 205
11.4.1
Calculating lnf
......................... 206
11.4.2
Calculating
g
.......................... 207
11.4.3
Calculating
H
.......................... 208
11.4.4
Results at last
.......................... 209
12
Writing do-files to maximize likelihoods
213
12.1
The structure of a do-file
......................... 213
12.2
Putting the do-file into production
................... 214
13
Writing ado-files to maximize likelihoods
217
13.1
Writing estimation commands
...................... 217
13.2
The standard estimation-command outline
............... 219
13.3
Outline for estimation commands using ml
............... 220
13.4
Using ml in noninteractive mode
.................... 221
13.5
Advice
................................... 222
13.5.1
Syntax
.............................. 223
13.5.2
Estimation subsample
..................... 225
χ
Contents
13.5.3
Parsing with help from mlopts
................. 229
13.5.4
Weights
............................. 232
13.5.5
Constant-only model
...................... 233
13.5.6
Initial values
........................... 237
13.5.7
Saving results in e()
...................... 240
13.5.8
Displaying ancillary parameters
................ 240
13.5.9
Exponentiated coefficients
................... 242
13.5.10
Offsetting linear equations
................... 244
13.5.11
Program properties
....................... 246
14
Writing ado-files for survey data analysis
249
14.1
Program properties
............................ 249
14.2
Writing your own predict command
................... 252
15
Other examples
255
15.1
The logit model
.............................. 255
15.2
The
probit
model
............................. 257
15.3
Normal linear regression
......................... 259
15.4
The Weibull model
............................ 262
15.5
The Cox proportional hazards model
.................. 265
15.6
The random-effects regression model
.................. 268
15.7
The seemingly unrelated regression model
............... 271
A Syntax of ml
285
В
Likelihood-evaluator checklists
307
B.I Method If
................................. 307
B.2 Method dO
................................. 308
B.3 Method dl
................................. 309
B.4 Method d2
................................. 311
B.5 Method lfO
................................. 314
B.6 Method lfl
................................. 315
B.7 Method If2
................................. 317
Contents xi
С
Listing
of estimation commands
321
C.I The logit model
.............................. 321
C.2 The
probit
model
............................. 323
C.3 The normal model
............................ 325
C.4 The Weibull model
............................ 327
C.5 The Cox proportional hazards model
.................. 330
C.6 The random-effects regression model
.................. 332
С
7
The seemingly unrelated regression model
............... 335
References
343
Author index
347
Subject index
349
|
| any_adam_object | 1 |
| author | Gould, William 1952- Pitblado, Jeffrey Poi, Brian |
| author_GND | (DE-588)1059407698 |
| author_facet | Gould, William 1952- Pitblado, Jeffrey Poi, Brian |
| author_role | aut aut aut |
| author_sort | Gould, William 1952- |
| author_variant | w g wg j p jp b p bp |
| building | Verbundindex |
| bvnumber | BV037331669 |
| classification_rvk | MR 2100 ST 601 |
| ctrlnum | (OCoLC)700334195 (DE-599)HBZHT016683735 |
| discipline | Informatik Soziologie |
| edition | 4. ed. |
| format | Book |
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| id | DE-604.BV037331669 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:22:15Z |
| institution | BVB |
| isbn | 1597180785 9781597180788 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022485561 |
| oclc_num | 700334195 |
| open_access_boolean | |
| owner | DE-188 DE-M382 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-703 DE-20 DE-N2 DE-384 |
| owner_facet | DE-188 DE-M382 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-703 DE-20 DE-N2 DE-384 |
| physical | XXII, 352 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Stata Press [u.a.] |
| record_format | marc |
| spelling | Gould, William 1952- Verfasser (DE-588)1059407698 aut Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; Brian Poi 4. ed. College Station, Tex. Stata Press [u.a.] 2010 XXII, 352 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 s Stata (DE-588)4617285-3 s DE-604 Maximum-Likelihood-Schätzung (DE-588)4194624-8 s Pitblado, Jeffrey Verfasser aut Poi, Brian Verfasser aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022485561&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gould, William 1952- Pitblado, Jeffrey Poi, Brian Maximum likelihood estimation with Stata Statistische Analyse (DE-588)4116599-8 gnd Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4116599-8 (DE-588)4194624-8 (DE-588)4617285-3 |
| title | Maximum likelihood estimation with Stata |
| title_auth | Maximum likelihood estimation with Stata |
| title_exact_search | Maximum likelihood estimation with Stata |
| title_full | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; Brian Poi |
| title_fullStr | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; Brian Poi |
| title_full_unstemmed | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; Brian Poi |
| title_short | Maximum likelihood estimation with Stata |
| title_sort | maximum likelihood estimation with stata |
| topic | Statistische Analyse (DE-588)4116599-8 gnd Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd Stata (DE-588)4617285-3 gnd |
| topic_facet | Statistische Analyse Maximum-Likelihood-Schätzung Stata |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022485561&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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