Regression Analysis of Count DataCambridge University Press, 28 sept. 1998 - 411 pages Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, and quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages. |
Table des matières
IV | 1 |
V | 3 |
VI | 8 |
VII | 10 |
VIII | 15 |
IX | 17 |
X | 19 |
XI | 20 |
LXI | 226 |
LXII | 234 |
LXIII | 238 |
LXIV | 240 |
LXV | 242 |
LXVI | 244 |
LXVII | 245 |
LXVIII | 246 |
XII | 22 |
XIII | 27 |
XIV | 37 |
XV | 44 |
XVI | 50 |
XVII | 57 |
XIX | 59 |
XX | 61 |
XXI | 70 |
XXII | 77 |
XXIII | 79 |
XXIV | 85 |
XXV | 88 |
XXVI | 93 |
XXVII | 94 |
XXVIII | 95 |
XXIX | 96 |
XXX | 97 |
XXXI | 106 |
XXXII | 112 |
XXXIII | 117 |
XXXIV | 121 |
XXXV | 123 |
XXXVI | 128 |
XXXVII | 134 |
XXXVIII | 135 |
XXXIX | 136 |
XL | 137 |
XLI | 139 |
XLII | 140 |
XLIII | 151 |
XLIV | 158 |
XLV | 163 |
XLVI | 168 |
XLVII | 182 |
XLVIII | 185 |
XLIX | 187 |
L | 188 |
LI | 189 |
LII | 190 |
LIII | 192 |
LIV | 207 |
LV | 216 |
LVI | 218 |
LVII | 219 |
LVIII | 220 |
LIX | 221 |
LX | 222 |
LXIX | 250 |
LXXI | 251 |
LXXII | 252 |
LXXIII | 256 |
LXXIV | 260 |
LXXV | 263 |
LXXVI | 269 |
LXXVII | 272 |
LXXVIII | 273 |
LXXIX | 275 |
LXXX | 276 |
LXXXI | 280 |
LXXXII | 287 |
LXXXIII | 290 |
LXXXIV | 293 |
LXXXV | 294 |
LXXXVI | 299 |
LXXXVII | 300 |
LXXXIX | 301 |
XC | 302 |
XCI | 307 |
XCII | 309 |
XCIII | 313 |
XCIV | 323 |
XCV | 324 |
XCVI | 325 |
XCVII | 326 |
XCVIII | 331 |
XCIX | 336 |
C | 343 |
CI | 344 |
CII | 345 |
CIII | 350 |
CIV | 356 |
CV | 358 |
CVI | 364 |
CVII | 367 |
CIX | 369 |
CX | 371 |
CXI | 374 |
CXII | 375 |
CXIII | 376 |
CXIV | 378 |
379 | |
399 | |
404 | |
Expressions et termes fréquents
alternative analysis application approach assumption asymptotically bivariate bootstrap Chapter coefficients component conditional mean function consider consistent estimator correctly specified count data models count models covariates defined denote density dependent variable deviance doctor visits Econometrics effects model equation example exponential family finite mixture first-order conditions fixed effects frequency given Gurmu Hausman heteroskedasticity hurdle model individual Journal likelihood function linear model LM test log-likelihood maximum likelihood estimation measurement errors methods misspecification mixture model multivariate NB2 model negative binomial nonlinear normal number of events observations obtained ordered probit overdispersion parameters Poisson distribution Poisson model Poisson PMLE Poisson process Poisson regression Poisson regression model polynomial probability random effects random effects model random variable regressors residuals sample serial correlation standard errors test statistic truncated underdispersion unobserved heterogeneity variance function variance matrix vector y₁ zero