Bishop Y.M.M., Feinberg S.E., Holland P.W. — Discrete Multivariate Analysis, Theory and Practice :: Электронная библиотека попечительского совета мехмата МГУ

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Bishop Y.M.M., Feinberg S.E., Holland P.W. — Discrete Multivariate Analysis, Theory and Practice

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Название: Discrete Multivariate Analysis, Theory and Practice

Авторы: Bishop Y.M.M., Feinberg S.E., Holland P.W.

Язык:

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1975

Количество страниц: 593

Добавлена в каталог: 25.06.2008

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

Предметный указатель

Hartley, H.O.      439 539
Hedayat, A.      215 534
Henry, N.W.      344 537
Hierarchy of models      320—324
Hierarchy principle      33 38 67—68
Hilferty, M.M      527 541
Hitchcock, S.E.      358 536
Hoel, P.G      270 536
Hogg, R.V      65 536
Holland, P.W.      370 404 405 407 412 413 414 416 417 418 422 423 429 433 493 518 534 536 541
Homeogeneity, marginal      54—55 282
Homeogeneity, of proportions      347
Homeogeneity, test for      293—296 306—309
Homogeneous Markov chain      262
Hypergeometric distribution, multivariate      450—452
Hypergeometric distribution, univariate      488—450 (see also “Exact theory”)
Incomplete arrays, for subsets of complete arrays      111 206—210 225—228
Incomplete arrays, multiway      210—225
Incomplete arrays, square tables and      283 290
Incomplete arrays, triangular      31
Incomplete arrays, two-way      178—206
Independence, as lack of association      374—380
Independence, in rectangular array      28—29
Independence, in three-way array      3 8
Independence, quasi      178—182 287—293
Independence, surface of      51
Indirect estimates      see “Fitting iterative “Partitioning
Information theory, minimum discrimination information (MDI)      344—347
Information theory, model generation      345—346
Information theory, modified MDI (MMDI)      295—296 347
Interaction, assessing magnitude of      146—155
Interaction, test for      146—155 364—366
Ireland, C.T.      85 284 295 296 345 346 347 348 536
Irregular arrays      21 48 (see
Isolates      193
Iterative proportional fitting      83—102 188—191
Iterative scaling      see “Iterative proportional fitting”
Jackknifmg      319
Jackson, P.H.      403 539
James, W.      403 536
Johnson, B.M.      407 536
Johnson, N.L      435 438 439 456 536
Johnson, W.D      143 353 536
Jones, R.      196 221 222
Joshii, S.W      435 539
Judge, G.G.      261 537
K, sum of Dirichlet parameters      401—402 405—408 420—426 K”)
Kalton, G      132 537
Kannel, W.B      358 359 360 533 541
Kastenbaum.M.A.      84 178 200 537 540
Katz, L.      277 278 279 537
Kemeny, J.      312 537
Keyfitz, N.      247 533
Kitagawa, E.M.      132 537
Kleinman, J.C      344 370 537
Koch, G G      143 284 306 308 353 354 532 536 537
Kondo, T      519 537
Korff, F.A      90 537
Kotz, S.      435 438 439 456 536
Kruskal, J.B      298 537
Kruskal, W.H      373 374 375 376 380 387 388 389 391 393 502 535 536 537
Ku, H.H.      127 143 148 284 295 296 344 346 347 348 363 536 537
Kucera, H.      331 537
Kulkarni, S.R      370 531
Kullback — Liebler distance      345 (see also “Information theory” “Likelihood
Kullback, S.      85 127 143 148 284 295 296 299 306 344 345 346 347 348 363 536 537
Kupperman, M.      127 363 537
Lambda, as measure of association      388—389
Lambda, as weight for Dirichlet prior      405—406 419—426 429—433
Lancaster, H.O.      1 2 244 261 262 262 522
Larntz, K.      205 522
Larzarsfeld, P.F.      256 265 225 344 532
Latent structure analysis      344
Lauh, E.      142 522
Least squares, weighted      352—357
Lee, T.C.      261 353 522 542
Lehman, E.      265 463 522 522
Lemon, R.E.      225 532
Leonard, T.      403 404 422
Levine, J.H.      100 101 532
Lewis, C.      402 522
Li, C.C      242 532
Lieberman, G.      449 522
Light, R.J.      224 220 221 226 222 400 523
Likelihood ratio statistic,       125—130
Likelihood ratio statistic, , asymptotic distribution of      513—516
Likelihood ratio statistic, , calculus for partitioning      169—175
Likelihood ratio statistic, , compared with Pearson’s chi-square      125—126
Likelihood ratio statistic, , conditional breakdown of      126—127
Likelihood ratio statistic, , conditional test for marginal homogeneity      293—294 307
Likelihood ratio statistic, , definition of      58
Likelihood ratio statistic, , for direct models      129—130 158—160
Likelihood ratio statistic, , for incomplete tables      191
Likelihood ratio statistic, , for indirect models, bounds for      160—161
Likelihood ratio statistic, , for symmetry      283
Likelihood ratio statistic, , structural breakdown of      127—130
Lin, Y.S      400 528
Lincoln, F.C.      233 538
Lindley, D.V.      402 538
Lindon, J.A.      340
Linear contrast, asymptotic variance of      494—497
Linear contrast, interpretations of      15 16 26—27 181 211—222 “u-terms”)
Linear models      see “Additive models”
Loeve, M.      463 464 462 538
Log-linear model, for $2 \times 2\times 2$ table      32—33
Log-linear model, for $2\times 2$ table      16—17 179
Log-linear model, for $I\times J$ table      24—26 179
Log-linear model, for $I\times J\times K$ table      35—41
Log-linear model, for $I\times J\times K$ table, interpretation of      37—39
Log-linear model, general      42—47
Log-linear model, general, interpretation of      45—47
Log-linear model, random effects models      371
Log-linear model, with terms of uniform order      119—121 156—158 “Cross “Direct
Logistic response function, multivariate      360
Logistic response function, univariate      357—360 (see also “Logit model”)
Logit model, alternatives to      367—371
Logit model, continuous variable      358
Logit model, definition of      22—23
Logit model, discrete variable      3 5 7—3 61
Logit model, for $2\times J$ table      30
Logit model, minimum chi square for      355—357
Logit model, mixed variable      358
Loglinear contrasts      see “Linear contrast”
Loops, closed      76
Lowe, C.R.      102 102 523
Lyell, L.P.      358 533
MacMahon, B.      102 103 538
MacWilliams, H.K.      152 153 533
Madansky, A.      260 261 294 295 301 306 307 538
Mantel — Haenszel test      133 147—148 151
Mantel, N.      124 146 173 193 194 197 247 255 366 531 537 538
Marginal homogeneity      see “Homogeneity”
Margins, lines of constant      53
Margins, measures insensitive to      392—393 (see also “Cell values” “Configuration”)
Margolin, B.H.      374 390 391 538
Markov models, assessing order of      269—270
Markov models, assessing order of, for aggregate data      260—261
Markov models, for cross classified states      273—279
Markov models, for different strata      277—279
Markov models, higher order      267—270
Markov models, single sequence transitions of      270—273
Markov models, time-homogeneity      261—267 269
Marx, T.J.      400 538
Maximum entropy      345—346 (see also “Information theory”)
Maximum likelihood estimates (MLEs), advantages of      58
Maximum likelihood estimates (MLEs), asymptotic behavior of      509—513
Maximum likelihood estimates (MLEs), conditional for closed populations      237—238
Maximum likelihood estimates (MLEs), existence of, for incomplete tables      186 216
Maximum likelihood estimates (MLEs), expansion of, for multinomial      511—513
Maximum likelihood estimates (MLEs), for marginal homogeneity      294—295 306

Maximum likelihood estimates (MLEs), for Markov chain transition probabilities      262—263 267—268
Maximum likelihood estimates (MLEs), for quasi-symmetry      289 306—319
Maximum likelihood estimates (MLEs), for symmetry      283 301—302
Maximum likelihood estimates (MLEs), methods of obtaining      73—83
Maximum likelihood estimates (MLEs), relative precision of      313—315
Maximum likelihood estimates (MLEs), suitability for likelihood ratio statistic      125—126
Maximum likelihood estimates (MLEs), uniqueness of, in incomplete tables      185
Maximum likelihood estimates (MLEs), when model is incorrect      513 (see also “Fitting Iterative proportional” “Birch’s “Direct
Maxwell, A.      1 538
Mayhall, J.T.      333 539
McNemar test      258 285
McNemar, Q.      258 285 309
MDI      see “Information theory”
Mendel, G      312 327 328 329 538
Menzerath, P.      272 538
mgf      see “Moment generating function”
Miller, G.A.      261 538
Minimax estimates      407
Minimum distance measure, asymptotic variance of      518—519
Minimum distance measure, methods      504—505 (see also “Pearson’s chi square” Neyman’s “Minimum
Minimum logit $\chi^2$       355—357
Mitra, S.K      518 538
MLE      see “Maximum likelihood estimates”
MMDI      see “Information theory”
Model, selection of      155—168
Model, simplicity in building a      312—313 (see also “Log-linear model” “Additive
Molina, E.C.      439 538
Moloney, W.C.      124 125 148 540
Moment generating function, to show convergence      469 (see also “Sampling distribution”)
Moments, limits of      485
Moments, methods of      505—507
Moments, order of      485—486 (see also “Binomial distribution moments “Hypergeometric “Multinomial “Poisson
Morgan, J.N.      360 540
Morris, C.      403 410 442 534 538
Morrison, A.S.      102 103 538
Mosteller, F.      15 100 101 105 106 134 135 136 260 262 316 317 318 319 327 331 332 337 368 375 401 436 492 532 538 539
Muller, T.P      333 539
Multinomial distribution, asymptotic normality of      469—472
Multinomial distribution, asymptotic variance of loglinear contrasts for      494—497
Multinomial distribution, estimation for      504—505
Multinomial distribution, formal stochastic structure for      480—481
Multinomial distribution, general theory for estimating and testing with a      502—508
Multinomial distribution, goodness-of-fit tests      507—508
Multinomial distribution, negative multinomial      454—456
Multinomial distribution, product multinomial      61 70—71
Multinomial distribution, sampling distribution      61 441—446
Multinomial distribution, variance of log-linear contrasts      494—497
Multiplicative models      225—228 320—324
Multiplicative models, estimating the mean of      403
Multivariate normal distribution, asymptotic distribution for multinomial      469—472
Murthy, V.K      524 539
Myerowitz, R.L.      124 125 148 540
Nerlove, M      360 539
Nested models, difference in       524—526 (see also “Likelihood ratio statistic
Newman, E.B.      270 271 272 273 539
Neyman, J.      58 349 351 454 539
Neyman’s chi square, minimizing      349—350
Neyman’s chi square, with linear constraints      351—352
Neyman’s chi square, with linear constraints, equivalence to weighted least squares      352—354
Noninteractive cell      181—182 193—194
Normal approximations to the binomial      464—465
Norming measures of association      375—376
Notation for models, dimension-free      61—62 (see also “O o
Novick, M.R      403 539
Novitski, E.      200 201 202 205 539
O, o notation, Chernoff — Pratt      479—480 481
O, o notation, conventions in use of      459—460
O, o notation, definitions      458—459 460
O, o notation, generalized for stochastic sequences      475—484
O, o notation, related to order of moments      485—486
Occurrence in probability      482—484
Odoroff, C.L.      140 539
Olson, N.C.      489 539
Oslick, A.      99 531
Outliers      140—141
Owen, D.B      439 449 537 539
P      see “Pearson’s measures of association”
Panel studies      257—260 265—267
Parameters      see “u-terms”
Partial association models, degrees of freedom for      121—122
Partial association models, fitting      161—165
Partitioning chi square      524—526
Partitioning chi square, calculus of Goodman      169—175
Partitioning chi square, Lancaster’s method      361—364
Partitioning chi square, likelihood ratio statistic      524—526
Pasternack, B.      116 117 118 541
Patil, G.P.      435 539
Pdf (probability density function)      63
Pearson, E.S.      439 539
Pearson, K      339 382 385 423 425 539
Pearson’s chi square, asymptotic distribution of      472—474
Pearson’s chi square, asymptotic relation to other statistics      513—516
Pearson’s chi square, compared with       124—125
Pearson’s chi square, correction for continuity      124 258
Pearson’s chi square, definition of      57
Pearson’s chi square, incomplete tables      191
Pearson’s chi square, large samples and      329—332
Pearson’s chi square, limiting distribution of, for complex cases      520—522
Pearson’s chi square, limiting distribution of, for complex cases, under alternative      329—332 518
Pearson’s chi square, limiting distribution of, for complex cases, under null model      516—518
Pearson’s chi square, minimizing      348—349
Pearson’s chi square, noncentral distribution      473—474 518
Pearson’s measures of association, $\Phi^2$       330 385—387
Pearson’s measures of association, P      382—383 385—387
Peizer, D.B.      457 539
Percentage points, for x distribution      527—529
Petersen, C.G.J.      232 539
Pfaffinan, C.      220 221 534
Pincus, G.S.      124 125 148 540
Plackett, R.L      15 124 353 365 366 375 539
Ploog, D.W.      203 539
Poisson distribution, asymptotic distribution of powers of mean      489—490
Poisson distribution, relationship to multinomial      440—441 446—448
Poisson distribution, sampling distribution      62 185—186 438—441 446—448
Poisson distribution, truncated variates      503—504 506 607 521—522 523—524
Poisson distribution, variance-stabilizing transform for      492 (see also “Frequency of frequencies” “Hansen “Maximum “Sampling
Pool, I.      107
Population-size estimation      229—256
Poskanzer, D.      196 221 222
Pratt, J.      457 475 480 481 483 539 540
Pratt, occurrence in probability      482—484 (see also “Stochastic sequence” “Notation dimension-free”)
PRE (proportional reduction in error)      387—389
PRECISION      313—315 (see also “Variance”)
Press, S.J.      360 539
Prince, T.      362
Probit transformation      367
Proctor, C.H.      277 278 279 537
Propagation of errors      see “Delta method”
Pseudo-Bayes estimators      408—410 419—423
Pseudo-Bayes estimators, asymptotic results for      410—416
Pseudo-Bayes estimators, small sample results for      416—419
PV (predictive value)      21—22
q      see “Association measures “Cochran’s “Yule’s
Quasi-independence      see “Independence”
Quasi-loglinear      210—212
Quasi-perfect mobility      206—208
Quasi-symmetry      see “Symmetry”
Rabinowitz, P.      410 533
Rao, C.R      58 472 473 474 505 525 540
Rasch, G.      344 540
Ratcliff, D      85 289 533
Rates, adjusting for multiple variables      123 133—136
Rates, direct standardization      131—132
Rates, generic form      133—136
Rates, indirect standardization      132
Rates, standard mortality ratio      132 135
Rearranging data, breaking apart an $I\times I$ table      207—209
Rearranging data, fitting by stages      107—108
Rearranging data, in $2\times 2$ table      11—16
Rearranging data, partitioning      105—107

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