Авторизация
Поиск по указателям
Gelman A., Carlin J.B., Stern H.S. — Bayesian data analysis
Обсудите книгу на научном форуме Нашли опечатку?
Название: Bayesian data analysisАвторы: Gelman A., Carlin J.B., Stern H.S.Аннотация: Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, added chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models, generalized linear mixed models, and more. The book is an introductory text and a reference for working scientists throughout their professional life.
Язык: Рубрика: Математика /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Издание: 2nd editionГод издания: 2004Количество страниц: 668Добавлена в каталог: 11.02.2006Операции: Положить на полку |
Скопировать ссылку для форума | Скопировать ID
Предметный указатель
Importance ratio 284 Importance resampling (sampling-importance resampling, SIR) 316 331 332 Importance resampling (sampling-importance resampling, SIR), examples 450 452 Importance resampling (sampling-importance resampling, SIR), unreliability of 316 Importance resampling (sampling-importance resampling, SIR), why you should sample without replacement 316 Importance sampling 342 348 Importance sampling for marginal posterior densities 343 450 Importance sampling, bridge sampling 344 348 Importance sampling, path sampling 344—345 348 Importance sampling, unreliability of 343 Improper posterior distributions see "Posterior distribution" Improper prior distribution see "Prior distribution" Imputation see "Multiple imputation" Inclusion indicator 201 518 Incumbency advantage 359—367 Incumbency advantage, two variance parameters 377 381 Indicator variables 370 Indicator variables for mixture models 463 Inference, finite-population and superpopulation 203 216 218 Inference, finite-population and superpopulation in ANOVA 408—409 Inference, finite-population and superpopulation, completely randomized experiments 219—221 241 Inference, finite-population and superpopulation, pre-election polling 211—212 Inference, finite-population and superpopulation, simple random sampling 208—209 Information matrix 102 107 Informative prior distributions see "Prior distribution" Institutional decision analysis 567—568 Instrumental variables 230—231 Intention-to-treat effect 230 231 Interactions in loglinear models 434 Interactions in regression models 370 549 Internet connect times 409—411 Intraclass correlation 391 Inverse probability 65 Inverse- 574 580 Inverse-gamma distribution 50 574 580 Inverse-Wishart distribution 87 574 581 Inverse-Wishart distribution, other models for covariance matrices 483—486 Iterative proportional fitting (IPF) 435—437 Iterative simulation 283—310 335—350 Iterative weighted least squares (EM for robust regression) 455 Iversen, E. 569 Jackknife 251 Jackman, S. 440 Jackson, P. 151 411 Jacobian 24 James, W. 150 James, W.H. 66 Jaynes, E. 28 29 66 68 113 494 Jeffreys' rule for noninformative prior distributions 62—63 66 69 Jeffreys, H. 28 66 68 Jeliazkov, I. 191 Jenkins, G. 494 Jiang, J. 272 514 Jittering 15 16 29 Johnson, N. 584 Johnson, V. 190 440 Johnson, W. 439 Johnstone, I. 66 Joint posterior distribution 73 Jones, D. 494 Kadane, J. 191 238 348 Kahneman, D. 28 568 Kaldor, J. 150 Karim, M. 439 Kass, R. 28 66 191 308 348 493 515 Katz, J. 30 385 Keller, J. 29 Kelly, A. 569 Kempt home, O. 411 Kennard, R. 412 Kennedy, A. 348 Kent, J. 95 King, G. 30 385 395 411 479 540 Kirby, A. 309 Kish, L. 193 241 539 Kiss, A. 440 Kleiner, B. 29 Knauss, W. 238 Knook, D. 539 Knuiman, M. 439 Kohn, R. 494 Kolaczyk, E. 494 Kong, A. 348 Kotz, S. 584 Krantz, D. 255 569 Kreft, I. 411 Kullback — Leibler information 107 586—588 Kullback — Leibler information, connection to deviance 181 191 Kullback, S. 589 Kutner, M. 385 Laird, N. 150 331 457 479 Landis, J. 539 Landwehr, J. 439 Lange, K. 457 458 Langevin updating for MCMC 336 Laplace's method for numerical integration 341—342 348 Laplace, P. 34 Laplace, R. 34 589 Large-sample inference 101—114 Larizza, C. 113 Latent continuous models for discrete data 419 Latin square experiment 220—221 Laud, P. 191 Lauritzen, S. 151 Lavine, M. 29 LD50 92—93 Le Cam, L. 589 Learner, E. 191 412 Lee, P. 68 Lehmann, E. 255 256 Leibler, R. 589 Leonard, T. 151 493 Lepkowski, J. 539 Lewis, C. 151 Lewis, J. 151 Leyland, A. 411 Li, B. 348 Liang, K. 439 Life expectancy, quality-adjusted 553 Likelihood 9—11 Likelihood principle 9 28 Likelihood principle, misplaced appeal to 198 Likelihood, complete-data 201 Likelihood, observed-data 202 Lin, C. 569 Linde, A. 191 194 Lindley, D. 27 28 30 113 151 237 411 Lindman, H. 113 237 Linear regression 353—387 see Linear regression, analysis of residuals 364 Linear regression, classical 355 Linear regression, conjugate prior distribution 382—385 Linear regression, conjugate prior distribution, as augmented data 383 Linear regression, correlated errors 372—375 Linear regression, errors in x and y 386 387 Linear regression, fitting two variance parameters 377 Linear regression, heteroscedasticity 372—382 Linear regression, heteroscedasticity, parametric model for 376 Linear regression, hierarchical 389—414 Linear regression, hierarchical, interpretation as a single linear regression 399 Linear regression, incumbency example 359—367 Linear regression, known covariance matrix 373 Linear regression, model checking 364 Linear regression, multivariate 481—482 Linear regression, multivariate, prior distributions 483—486 Linear regression, posterior simulation 357 Linear regression, prediction 358 367 Linear regression, prediction, with correlations 374 Linear regression, residuals 359 365 Linear regression, robust 455—457 Linear regression, several variance parameters 382 Linear regression, Student-t errors 455—457 Linear regression, weighted 376 Linear transformation, with Gibbs sampler for hierarchical regression 403 Link function 416 418 Little, R. 237 238 331 457 539 Little, T. 440 Liu, C. 309 331 348 457 458 539 540 Liu, J. 308 309 331 348 412 Location and scale parameters 64 Logistic regression 88—93 417 Logistic regression for multinomial data 430 Logistic regression, hierarchical 428—430 Logistic regression, latent-data interpretation 419 Logit (logistic, log-odds) transformation 24 146 Loglinear models 433—437 Loglinear models, prior distributions 434 Lognormal distribution 578 Lohr, S. 241 Longford, N. 411 Longitudinal data, modeling covariance matrices 485 Longitudinal data, survey of adolescent smoking 214—216 Louis, T. 28 66 494 Luce, R. 568 Lunn, D. 29 151 191 309 608 Lynn, J. 238 Mack, S. 440 539 Madigan, D. 191 412 Madow, W. 539 Makov, U. 480 Malec, D. 151 Malick, B. 515 Mallows, C. 28 191 Manton, K. 66 151 Map, as used in model checking 159 Maps of cancer rates 55 Maps, artifacts in 55—66 MAR (missing at random) 204 518 MAR (missing at random), a more reasonable assumption than MCAR 519 Mardia, K. 95 Marginal and conditional means and variances 23 142 Marginal posterior distribution 73 128 129 275 Marginal posterior distribution, approximation 324—325 Marginal posterior distribution, computation for the educational testing example 600—601 Marginal posterior distribution, computation for the survey incentives example 548 Marginal posterior distribution, computation using importance sampling 343 450 Marginal posterior distribution, EM algorithm 317—324 Markov chain 286 Markov Chain Monte Carlo (MCMC) 285—310 335—350 Markov chain Monte Carlo (MCMC), adaptive algorithms 307 Markov chain Monte Carlo (MCMC), assessing convergence 294—298 Markov chain Monte Carlo (MCMC), assessing convergence, between/within variances 296 Markov chain Monte Carlo (MCMC), assessing convergence, simple example 297 Markov chain Monte Carlo (MCMC), auxiliary variables 335—339 348 Markov chain Monte Carlo (MCMC), burn-in 295 Markov chain Monte Carlo (MCMC), data augmentation 303 Markov chain Monte Carlo (MCMC), efficiency 291 302—307 Markov chain Monte Carlo (MCMC), Gibbs sampler 287—289 292—294 308—309 Markov chain Monte Carlo (MCMC), Gibbs sampler, efficiency 302—305 Markov chain Monte Carlo (MCMC), Gibbs sampler, examples 288 300 380 400 449 474 538 Markov chain Monte Carlo (MCMC), Gibbs sampler, picture of 288 Markov chain Monte Carlo (MCMC), Gibbs sampler, programming in R 601—608 Markov chain Monte Carlo (MCMC), hybrid (Hamiltonian) Monte Carlo 335—336 348 Markov chain Monte Carlo (MCMC), inference 294—298 Markov chain Monte Carlo (MCMC), Langevin updating 336 Markov chain Monte Carlo (MCMC), Metropolis algorithm 289—292 308—309 Markov chain Monte Carlo (MCMC), Metropolis algorithm, efficient jumping rules 305—307 Markov chain Monte Carlo (MCMC), Metropolis algorithm, examples 290 301—302 Markov chain Monte Carlo (MCMC), Metropolis algorithm, generalizations 335—340 Markov chain Monte Carlo (MCMC), Metropolis algorithm, picture of 286 Markov chain Monte Carlo (MCMC), Metropolis algorithm, programming in R 604—606 Markov chain Monte Carlo (MCMC), Metropolis algorithm, relation to optimization 290 Markov Chain Monte Carlo (MCMC), Metropolis — Hastings algorithm 291 308—310 Markov chain Monte Carlo (MCMC), Metropolis — Hastings algorithm, generalizations 335—340 Markov chain Monte Carlo (MCMC), multiple sequences 294 Markov chain Monte Carlo (MCMC), output analysis 294—299 Markov chain Monte Carlo (MCMC), overdispersed starting points 295 Markov chain Monte Carlo (MCMC), perfect simulation 340 348 Markov chain Monte Carlo (MCMC), recommended strategy 307 308 Markov chain Monte Carlo (MCMC), regeneration 340 348 Markov chain Monte Carlo (MCMC), restarting methods 340 348 Markov Chain Monte Carlo (MCMC), reversible jump sampling 338—339 348 Markov chain Monte Carlo (MCMC), simulated tempering 337—338 348 Markov chain Monte Carlo (MCMC), slice sampling 336 348 Markov chain Monte Carlo (MCMC), thinning 295 Markov chain Monte Carlo (MCMC), trans-dimensional 338—339 348 Marquardt, D. 413 414 Martin, A. 440 609 Martz, H. 29 Matrix and vector notation 5 Maximum entropy 66 494 Maximum likelihood 247 MCAR (missing completely at random) 518 McClellan, M. 238 McCullagh, P. 348 439 514 McCulloch, R. 191 412 493 494 515 McGonagle, K. 568 McNeil, A. 309 McNeil, B. 238 Measurement error models, hierarchical 151 Measurement error models, linear regression with errors in x and y 387 Measurement error models, nonlinear 498—504 Medical screening, example of decision analysis 552—555 Melmon, K. 515 Meng, C. 151 Meng, X. 190 191 193 309 331 348 440 457 480 493 539 Mengersen, K. 348 494 Meta-analysis 151 156 Meta-analysis, beta-blockers study 145—150 488—491 Meta-analysis, bivariate model 488—491 Meta-analysis, goals of 147 Meta-analysis, survey incentives study 544—550 Metropolis algorithm 289—292 308—309 Metropolis algorithm, efficient jumping rules 305—307 Metropolis algorithm, examples 290 301—302 Metropolis algorithm, generalizations 335—340 Metropolis algorithm, picture of 286 Metropolis algorithm, programming in R 604—606 Metropolis algorithm, relation to optimization 290 Metropolis — Hastings algorithm 291 308—310 Metropolis — Hastings algorithm, generalizations 335—340 Metropolis, N. 308 Meulders, M. 190 Milliff, R. 494 Minimal analysis 222 Missing at random (MAR) 204 518 Missing at random (MAR), a more reasonable assumption than MCAR 519 Missing at random (MAR), a slightly misleading phrase 204 Missing completely at random (MCAR) 518 Missing data 517—540 Missing data and EM algorithm 521 523 Missing data, intentional 199 Missing data, monotone pattern 522 524—525 530—534 Missing data, multinomial model 533—534 Missing data, multivariate normal model 523—526 Missing data, multivariate t model 525 Missing data, notation 200 517—519 521 Missing data, paradigm for data collection 199 Missing data, Slovenia survey 534—539 Missing data, unintentional 199 207 517 Mixed-effects model 391 Mixture models 23 153 463—480 Mixture models, computation 467—468 Mixture models, continuous 464 Mixture models, discrete 463 Mixture models, exponential distributions 516 Mixture models, hierarchical 470 Mixture models, model checking 477 479 Mixture models, prediction 476 Mixture models, schizophrenia example 468—479
Реклама
distribution 
|
|
О проекте