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| Little R.J.A., Rubin D.B. — Statistical analysis with missing data |
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| Предметный указатель |
Ignorable sampling mechanisms 245
Implicit model 256
Imputation (see Filling in for missing values)
Income nonresponse 65 227—230
Incomplete-data likelihood 134
Incompletely-classified data (see Partially classified contingency tables)
Independent variables, missing values in 153—157 196 203
Inestimable parameters 119—124 239
Inference based on maximum likelihood, theory 84—88
Information/information matrix 85 98
Information/information matrix, complete 137—138
Information/information matrix, expected 85 96 128 137 138 145
Information/information matrix, fraction missing 137—138
Information/information matrix, missing 137—138 139 194 257
Information/information matrix, observed 85 96 128
Instrumental variables (see Heckman’s two-step method Restrictions
Interactions in contingency tables (see Association in
Interactions in regression 154
Interval estimation 84 105—107
Interval estimation, RLS 209—216
Ireland, C. T. 59 72
Irregularly-spaced time series (see Time series models)
Item nonresponse 13 15 16 60 66—67 247—248
Iterative methods 26 127—141 185
Iterative proportional fitting algorithm 204
Iterative proportional fitting algorithm to known margins (see Raking)
Iteratively reweighted least squares (see IRLS)
Jackknife 71
Jarrett, R. G. 22 27 34 36 37
Jeffreys’ prior for normal sample 104
Jenkins, G. M. 162 168
Jenkins, J. G. 19
Jennrich, R. 1 157 158 159 160 168 169
Jensen’s Inequality 135
Johnson, J. H. 71
Jones, R. H. 162 168
Kalfon, G. 60 65 72
Kalman filter models 162 166—168
Kalman, R. E. 162 165 168
Kaufman, L. 183 193
Kempthorne, O. 21 36
Kim, J. O. 43 48 49
Kish, L. 60 65 72
Kleinbaum, D. G. 41 48
Krzanowski, W. J. 203 216
Kullback, S. 59 72
Kulldorff, G. 22 241
Kupper, L. L. 41 48
Lack of fit 239 (see also Goodness-of-fit statistics)
Laird, N. M. 6 18 129 130 131 135 136 137 139 145 150 152 168 209 210 216 237 240 241 262 264
Langwell, K. 169
Laplace (double exponential) distribution 96
Large sample likelihood theory 84—86
Latent variable model 148—149
Latin square 26 37—38
Least squares analysis 21 38
Least squares analysis via EM 152—153
Least squares analysis, estimation 22—23 25—32
Ledolter, J. 165 168
Leprosy 177
Li, K. H. 255 264
Likelihood ratio statistic 87—88 158 161 186 192 205 217 239
Likelihood with missing data 89—90 218—220
Likelihood with monotone missing data (see Factored)
Likelihood, equation 81 128
Likelihood, function 79—81
Likelihood, ignoring the missing-data mechanism 89
Likelihood, test for MCAR 193
Lillard, L. 227 228 229 230 241
Lindley, D. V. 85 95 105 124
Linear estimators 70
Linear model 21—24 113
Linear model with missing outcomes 21—38 152—153 222—230 232—235
Linear model with missing predictors and outcomes 153—157 196 203
Linear regression (see Linear model)
Listwise deletion (see Complete-case methods)
Little, R. J. A. 6 19 57 58 62 71 72 92 95 104 107 124 129 139 144 145 154 155 168 169 170 193 195 198 200 205 207 209 212 215 216 217 230 235 237 241 242 245 248 250 254 261 264
Local maxima of the likelihood 200 206 208
Logistic regression model 57 196—206 230 254 262
Loglikelihood function 80—81
Loglinear models 172 185—193 203—206 235 241
Longitudinal study 4 157
Lord, F. M. 120 124
Lost information (see Information/information matrix missing)
Louis, T. A. 137 139
M step (maximization step) 130 (see also EM algorithm)
Madow, W. G. 50 51 71 72 254 262 264
Main effects in contingency tables 185
MAR (see Missing at random (MAR))
Marini, M. M. 7 8 19 109 125
Markov model 129
Matching of files 121
Matching to fill in respondent values (see Hot deck imputation)
Matthai, A. 42 48
Maximizing over the missing data 92—95
Maximum likelihood (ML) 12 37 81—84 98 Factored Likelihood)
MCAR (see Missing completely at random (MCAR))
McCullagh, P. 171 193
McKendrick, A. G. 129 139
Mean imputation 6 44—47 60 61—62 74
Mechanisms leading to missing data (see Ignorable missing-data mechanism Nonignorable
Mehra, R. K. 165 168
Meltzer, A. 166 167 169
Missing at random (MAR) 10 14—17 90
Missing completely at random (MCAR) 14—17 39
Missing information (see Information matrix missing)
Missing information principle 129 (see also EM algorithm)
Missing values as parameters 92—95 126
Missing-data indicator 89
Missing-plot techniques (see Analysis of variance (ANOVA))
Missing-value covariates 27—36
Misspecification of model (see Sensitivity of inference)
Mixed normal and nonnormal data 195—217
Mixed-effects analysis of variance 149 157—161
Mixed-up bivariate normal sample 93
Mixture models 207—216
Mixture models for respondent and nonrespondent strata 230—235 262—264
ML (see Maximum likelihood (ML))
Model-based methods 7
Model-based methods for surveys 52—53 244—265
Monotone pattern of missing data 4 7 8 14 17 19 98—119 172—181 188 215 244 248
Monotone pattern of missing data for bivariate counted data 174—181
Monotone pattern of missing data for multivariate normal data 109—112
More observed variables 7 14 119 207
Morgensfem, H. 41 48
Morrison, D. F. 125
Multinomial data 4—5 131—132 138 171—194 196
Multinomial data, distribution 95 265
Multiple correlations 124
Multiple imputation 61 255—259 264 265
Multiple linear regression 84 142 152—157 Normal
Multiple maxima 200 207 208
Multivariate analysis of variance 203—206
Multivariate analysis of variance, data 16—18 39—49
Multivariate analysis of variance, normal 81 82 140 142—170
Multivariate analysis of variance, normal mixtures 207
Multivariate analysis of variance, normal monotone data 109—112 116—119
Multivariate analysis of variance, regression 155—157 215—216
Multivariate analysis of variance, t samples 211—216
Multiway tables (see Categorical data)
Muscative Coronary Risk Factor Study 4
Nearest-neighbor hot deck 65—66
Nested missing-data pattern (see Monotone pattern of missing data)
Nested models 87
Never jointly observed 120
Newton — Raphson algorithm 128 130 140 146 158 165
Nie, N. H. 6 19
Nisseison, H. 72 262 264
Noncontact (see Unit nonresponse)
Nonignorable adjustment cell model 260—261
| Nonignorable missing-data mechanism 8—13 19 89 90 218—241 248 259—264
Nonignorable missing-data mechanism with follow-ups 243 262—264
Nonignorable missing-data mechanism, sampling mechanism 246
Noninterview adjustments (see Unit nonresponse)
Nonlinear estimators 71
Nonlinear regression 93
Nonrandomly missing data (see Nonignorable missing-data mechanism)
Nonrandomly missing data as random subsampling 54
Nonrandomly missing data, mechanism (see Ignorable missing-data)
Nonrandomly missing data, strata 3 53 219—220
Nonrandomly missing data, weights 58
Nordheim, E. V. 235 241
Normal data 196—217 222—235
Normal data, censored 223 224—225
Normal data, grouped with covariates 222—223
Normal data, linear regression model 83 99—101 196—217
Normal data, nonignorable models 223—230
Normal data, sample 82—83 93—94
Observed at random (OAR) 14—17
Observed likelihood 134
Odds ratio 41 48 194
Odds ratio of response rates 75
Oh, H. L. 53 56 62 72 74
Olkin, I. 72 196 216 262 264
Olsen, A. R. 7 8 19 109 125
Olsen, R. J. 230 242
Optimal, asymptotic properties 86
Orchard, T. 6 19 129 138 139 144 169
Orthonormal linear combinations 34—36
Outliers 210 215
Oxspring, H. H. 177 179 193
p-value 87 192
Pairwise available-case methods 42
Panel study 7 40 109—112
Parameters of conditional association 120
PariS, R. 71
Partial correlation 120—124
Partial information (see Information matrix observed)
Partially classified contingency tables 14 171—194 235—241
Pattern of missing data 4 248
Patterned covariance matrices 146—148
Pearce, S. C. 27 36
Pearson chi-squared statistic 186 192
Petrie, T. 139
Pettitt, A. N. 211 216
Phillips, G. D. A. 162 168
Pivoting (see Sweep operator (SWP))
pixels 183
Poisson data 80 82 173
Polynomials of regressors 154
Positron emission tomography (PET) 183—185
Posterior distribution (see Bayesian inference)
Poststratification 58 66 251 253 258 259 265
Poststratified estimator of the mean (see Poststratification)
Potthoff, R. F. 158 159 169
Power transformation 227
PRECISION 180—181
Precision of estimation from filled-in data 255—259 (see also Asymptotic covariance matrix of parameters or estimates)
Predicting missing values (see Filling in for missing values)
Predictive Bayesian approach 230—235 (see also Multiple imputation)
Predictive distribution 231 256
Preece, D. A. 27 36
Pregibon, D. 235 242 261 264
Press, S. J. 93 95
Principal component analysis 142
Prior distribution (see Bayesian inference)
Probability of response (see Response propensity)
Probability sampling 9 51 245
Probit regression of response 57 229—230 with
Propensity scores 57
Propensity scores, stratification on 74
Proper imputation 259
Q-function 134 (see also EM algorithm)
Quasi-randomization inference 53—75
Quick missing-data adjustments 39 49
Raked estimate 59 74
Raking 59 74—75
Random effects model 149 158 161
Random effects model for time series 165
Random sampling with replacement 69
Randomization inference for surveys 6 50—75
Randomized block 26 31 35—36
Randomly missing data (see Missing completely at random (MCAR) Missing
Rao, C. R. 35 36 82 84 95 135 139
Ratio estimator 254
Ratios, imputation of 67
Reece, J. S. 230 241
Refined and coarse classifications 177—179
Refusal to answer 1 (see also Income nonresponse)
Regression 9 41 43 48 49 83 84 108 116 152—157 170
Regression, estimator 102 254
Regression, imputation 6 45—47 61 253—255 Filling
Regression, interactions in 154 (see also Linear model)
Regular exponential family 96 138—139 143
Reinisch, J. M. 155 169
Repeated imputations (see Multiple imputation)
Repeated measured model 157—161
Replacement units 60
Residuals, added to imputations 61 (see also Filling in for missing values stochastically to
Response indicator matrix 19 89
Response propensity 57
Response rates 56 (see also Pattern of missing data)
Restricted covariance matrix 146—152 157—161
Restrictions on regression coefficients 224 226
Reverse sweep (RSW) 115 126
Reviews of missing-data literature 6 39 262
Robust estimation 209—216
Robust estimation, inference 263 (see also Sensitivity of inference)
Robust estimation, regression 211—216
Robust estimation, variances, correlations 209—216
Rosenbaum, P. R. 57 72
Rosenblum, L. A. 169
Rounded data (see Grouped and rounded data)
Roy, S. N. 158 159 169
Royall, R. M. 254 264
Rubin, D. B. 6 7 8 14 18 19 30 31 33 34 37 43 48 57 61 66 71 72 89 90 93 95 109 117 119 120 121 122 124 125 126 129 130 131 135 136 137 139 145 147 148 149 150 152 168 169 170 183 193 208 209 210 211 212 216 229 230 231 232 234 242 244 245 246 249 250 254 255 256 257 258 259 262 264
Samdal, C. E. 58 71 73
Sample mean vector and covariance matrix 17
Sample surveys 16 50—75
Sampling frame 9
Sampling mechanism 245
Sampling variance 67—71
Sampling weight 55
Samuhel, M. E. 71 241
Sande, I. G. 66 71 72
Satterthwaite, F. E. 257
Saturated model 188
Schenker, N. 255 257 258 259 265
Scheuren, F. S. 53 56 61 62 72 74
Schieber, S. J. 61 72
Schluchter, M. D. 157 158 159 160 168 195 198 200 205 207 216 217
Schulzinger, M. F. 169
Score function 81 128
Scoring algorithm 128 130 145
Scott, A. J. 93 95 246 265
selection model 223—230 242 262—264
Sensitivity of inference 225 231—235 259 261
Sensitivity of inference with followups 262—264
Sex differences 155—157
Shepp, L. A. 183 193
Shumway, R. H. 162 165 166 167 169
Significance levels (see P-value)
Simple random sampling 51 53
Simulation of posterior distributions 105—107
Simulation studies of missing-data methods 43 215 262—264
Small-sample inference 104—107 125
Smith, H. 23 36 117 124
Smith, J. P. 227 228 229 230 241
Smoothing 165 253
Snedecor, G. W. 23 37 101 125 150 169
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