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PROBABILITY AND RANDOM VARIABLES
PROBABILITY DISTRIBUTIONS AND STATISTICAL MODELS
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AIC values AIC,s AIC(fiv AIC(k Akaike analysis approximation assume autoregressive binomial distribution called Chapter COMPUTATION conditional distribution model conditional log likelihood constraint contingency table CONTINUE data set defined denote DIMENSION distribution function expected log likelihood explanatory variables F0RMATC1H Figure free parameters frequency histogram frequency table HOUSEHOLDER TRANSFORMATION IFCIEXP.EQ.1 IFCKU independent INPUTS Institute of Statistical K-L information quantity likelihood is given matrix maximum likelihood estimator maximum log likelihood mean expected log MODEL(l multinomial distribution normal distribution normal distribution model normal random number number of free NUMBER OF REGRESSORS objective variable obtained order model OUTPUTS p(iv Poisson distribution polynomial regression model probability density function probability distribution probability mass function problem procedure random variable READING FORMAT regression coefficients REGRESSORS residual variance respectively response variable RETURN END Sakamoto sample set of data Statistical Mathematics SUBROUTINE Tokyo true distribution two-way table URANCKU WRITEC