Hosmer D.W., Lemeshow S. — Applied logistic regression :: Электронная библиотека попечительского совета мехмата МГУ

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Hosmer D.W., Lemeshow S. — Applied logistic regression

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Название: Applied logistic regression

Авторы: Hosmer D.W., Lemeshow S.

Аннотация:

A textbook for part of a graduate survey course, courses of a quarter or semester, and focused short courses for working professionals. Assuming a solid foundation in linear regression methodology and contingency table analysis, biostaticians Hosmer (U. of Massachusetts- Amherst) and Lemeshow (Ohio State U.) introduce the logistic regression model and its use in methods for modeling the relationship between a categorical outcome variable and a set of covariates. The first edition appeared about a decade ago, and the second incorporates theoretical and computational developments since then.

Язык:

Рубрика: Математика/Вероятность/Статистика и приложения/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: second edition

Год издания: 2000

Количество страниц: 376

Добавлена в каталог: 03.06.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

Multinomial logistic regression model: coefficient estimation      264—273
Multinomial logistic regression model: components of      260—264
Multinomial logistic regression model: fit assessment      280—287
Multinomial logistic regression model: model-building strategies      273—280
Multiple logistic regression: confidence interval estimation      40—42
Multiple logistic regression: estimation methods      43—44
Multiple logistic regression: exercises      44—46
Multiple logistic regression: fitting the      33—36
Multiple logistic regression: model      31—33
Multiple logistic regression: testing for significance of      36—40
Multivariate analysis, variable selection      95—96
Multivariate logistic regression model: fitted      64—69
Multivariate logistic regression model: sample size      346—347
n $\times$ n diagonal matrix      212
n-asymptotics      144 176
NHANES (I/II/III)      214
Noise variables      121
Noniterative weighted least squares      21
Normal distribution: diagnostics      175
Normal distribution: logistic regression model      6—7 18
Normal distribution: stepwise logistic regression      116
Normal distribution: variable selection and      93
Null distribution      146
Odds ratio: adjusted      69
Odds ratio: correlated data analysis      322—325
Odds ratio: defined      49
Odds ratio: fitted logistic regression      51—55 68 74—79 81 83—84
Odds ratio: goodness-of-fit assessment      188—190 194—195
Odds ratio: matched case-control studies      224
Odds ratio: multinomial logistic regression      265—266
Ordinal logistic regression models: components of      288—305
Ordinal logistic regression models: model-building strategies      305—308
Osius — Rojek goodness-of-fit test      155
Outcome variable, defined      7. See also Specific types of regression models
Overfitting      92 134—135
p coefficients      36
p-value: best subsets logistic regression      131—132
p-value: computation methods, generally      333
p-value: goodness-of-fit assessment      146 152 154 188
p-value: logistic regression model      16—17 37
p-value: sampling models and      213—214
p-value: stepwise logistic regression      117—120 125
Partial likelihood ratio test      101—102
Partial proportional odds model      298
PASS      6 0 343
Pearson chi-square statistic, goodness-of-fit assessment      145—147 152—154 187 218 327.
Pearson correlation coefficient, goodness-of-fit assessment      164—165
Pearson deviance, goodness-of-fit assessment      145—147 152—154
Polychotomous independent variable, fitted regression analysis      56—72
Polychotomous logistic regression      260
Polychotomous variables, variable selection      116
pooling      136—137
Population average model, correlated data analysis      311—323 329—330
Prediction, goodness-of-fit assessment      160
Predictor variables      1
Preliminary final model      99
Preliminary main effects model      97
Probability distribution, exact methods      334—336
PROC LOGISTIC      133—134
Proportional hazards regression model      100—101 205
Proportional odds model      297—298
Prostate cancer study      25—26
Quasicomplete separation      139
Random effects models, correlated data analysis      310 320
Random variable, cumulative distribution      5—6
Recycling, in variable selection      102—103
Reference cell coding      54 57 59
Refinement stage, in variable selection      97
Refitting      97 99 339
Regression coefficients      220
Regression diagnostics, defined      167. See also Diagnostics
Regression sampling model, defined      204. See also Sampling models
Residual sum-of-squares      12—13 167—168
Response variable      1 12
Risk factor studies: computational methods, generally      332
Risk factor studies: correlated data analysis      323—324
Risk factor studies: goodness-of-fit assessment      152
Risk factor studies: multivariable logistic regression      69 74
Robust estimator      315—316
ROC (Receiver Operating Characteristic) Curve, goodness-of-fit assessment      160—164
S-shaped curve      5 69
Sample size: adjustment of      341
Sample size: continuous covariates      343—345
Sample size: dichotomous covariates      339—341
Sample size: multivariable logistic regression      343—347
Sample size: univariate logistic regression      343
Sample size: Wald statistic      341—342
Sample surveys, complex      211—221
Sampling models: case-control studies      205—210
Sampling models: cohort studies      203—205
Sampling models: complex sample surveys, fitting logistic regression models to data from      211—221
Sampling models: exercises      222
Sampling models: NHANES case illustration      214—221
SAS package      85 121 125 133 139 149 152 169—170 229 237 304 309 326—327
Saturated model      12—13
scatterplot      94 107

Score test (ST): best subsets logistic regression      133
Score test (ST): fitted logistic regression      85
Score test (ST): goodness-of-fit assessment      152 155
Score test (ST): logistic regression      16
Score test (ST): multiple logistic regression      39—40
Score test (ST): variable selection      125
Simulation tests      152 155
Slope coefficient, confidence interval: logistic regression      17—18 48
Slope coefficient, confidence interval: multiple logistic regression      37 40
Smoking and health study      205. See also NHANES (I/II/III)
Smoothing, goodness-of-fit      176
Software programs / packages: additive models      104
Software programs / packages: best subsets linear regression      96 133—134
Software programs / packages: correlated data analysis      309 316 319—320 325—327
Software programs / packages: fitted logistic regression      85
Software programs / packages: fractional polynomials      102
Software programs / packages: generally      331
Software programs / packages: goodness-of-fit assessment      151 153—154 169—170
Software programs / packages: logistic regression      15—16 19
Software programs / packages: matched case-control studies      228—229 233 243—244 248 250
Software programs / packages: model-building      139
Software programs / packages: multinomial logistic regression      266 269 277
Software programs / packages: multiple logistic regression      32
Software programs / packages: ordinal logistic regression models      302—304 308
Software programs / packages: sample size      343
Software programs / packages: sampling models      205 211—213 219—220
Software programs / packages: stepwise logistic regression      121 125
Software programs / packages: variable selection      94—95 102 104 121 125
SSE      12—13
SSR      12
Stata      94—95 102 121 125 135 139 151 153—154 169—170 211—213 219—220 229 243—244 250 266 269 302—304 308—309 316 319—320 325—327
Statistical significance, in variable selection      116 128
Stepwise logistic regression, model-building: benefits of      116
Stepwise logistic regression, model-building: continuous covariates      125—126
Stepwise logistic regression, model-building: fitting      117—120
Stepwise logistic regression, model-building: interactions      126—128
Stepwise logistic regression, model-building: likelihood ratio      122—123
Stepwise logistic regression, model-building: statistical significance      116 124
Stepwise logistic regression, model-building: variable selection      96 116—118 120—122
Stratified analysis: case-control studies      206 209
Stratified analysis: fitted logistic regression compared with      79—85
Stratified analysis: model-building and      136—137
Stukel's test      152 184
Subject-specific model, correlated data analysis      310 312
SUDAAN      211—212 219
Sum-of-squares: best subsets logistic regression      130—131
Sum-of-squares: goodness-of-fit assessment      165
t-test      93
Transitional models, correlated data analysis      312 328
Type II error      155
U-shaped function      97
UMARU IMPACT study (UIS), logistic regression illustration      0
UMARU IMPACT study (UIS), overview      26—28
UMARU IMPACT study (UIS), sample size      339
UMARU IMPACT study (UIS), variable selection      104—116
Univariate analysis      92—93
Univariate models, fitted linear regression      64
Unstructured model, correlated data analysis      313
Validation data      see External validation
Values, fitted      85—88
Variable selection, in model-building: case illustration, UIS study      104—116
Variable selection, in model-building: chi-square test      92—93 102
Variable selection, in model-building: continuous covariates      93—94
Variable selection, in model-building: continuous variables      93
Variable selection, in model-building: fractional polynomials      100—103
Variable selection, in model-building: generalized additive model      103—104
Variable selection, in model-building: independent variables      97—98
Variable selection, in model-building: interaction      98—99
Variable selection, in model-building: multivariable analysis      95—96
Variable selection, in model-building: overfitting      92
Variable selection, in model-building: stepwise procedure      96 116—128
Variable selection, in model-building: univariable analysis and      92—93 95
Variable selection, in model-building: weighted average      94
Vectors: best subsets logistic regression      128—129
Vectors: confidence interval estimation      40—41
Verifying, variable selection      97
Wald statistic      See Wald test statistic correlated data analysis
Wald test statistic: best subsets logistic regression      132
Wald test statistic: correlated data analysis      318
Wald test statistic: logistic regression model, generally      16
Wald test statistic: multinomial logistic regression      270 273
Wald test statistic: multiple logistic regression      38—39
Wald test statistic: variable selection      97 102 115—116
Wald tests: complex sample surveys      213 216 221
Wald tests: correlated data analysis      316
Wald tests: logistic regression      16 18
Wald tests: multiple logistic regression      37 39
Wald tests: statistic      See Wald test statistic
Wald tests: variable selection      125
Wald-based confidence interval      19—20
Weighted average      94
Weighted least squares linear regression      168
Weighted linear regression      153
Working correlation      313
Zero cell      93 136 139
Zero-one coding      54

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