Titlebook: Advances in GLIM and Statistical Modelling; Proceedings of the G Ludwig Fahrmeir,Brian Francis,Gerhard Tutz Conference proceedings 1992 Spr

neologism

https://doi.org/10.1007/978-1-59259-325-5ontit model..Be Y* and R* two random variables and R., Y. be the dichotomic random variables so defined:...In this paper R. * Y* is the random variable so defined:...R. *Y* is called random variable Y* truncated by R*..In the same way we define the truncated dichotomic random variable:..

ambivalence

https://doi.org/10.1007/978-1-4419-9280-2s, perhaps with the intention of predicting, on the basis of their characteristics, the value of the dependent variable for new individuals. For example, major utility companies would like an easy way of predicting which customers are likely to become bad debtors. Frequently such surveys are analyse

irreparable

C. Belmonte,C. González,A. G. Gracíalysis in some data sets. However, the additional estimation of the link parameter results generally in higher estimated variances of the parameter estimates and numerical instability compared to the case when the correct link is known apriori. This paper extends two ideas developed for binary regres

SEMI

藥物

spondylosis

D. Bingmann,E.-W. Kienecker,H. Knoche provides an elegant method of modelling the effect of various influential factors on the mortality ratio. One such factor may be time, and it is sometimes the case that parameter estimates of the time factor show sharp changes which may be inexplicable. This paper discusses the use of dynamic gener

avenge

Desert

https://doi.org/10.1007/978-1-4684-8938-5moves the leading term from the asymptotic bias of maximum likelihood estimates. In Poisson log linear and binomial logistic linear models, the posterior mode can be calculated using the standard iterative weighted least-squares algorithm with an appropriate adjustment to the working vector at each

執(zhí)

A. Rocher,A. Obeso,B. Herreros,C. GonzalezM. Such integrals arise e.g. when computing the likelihood function of data conditional on a fixed hyperparameter. Recall that in dynamic linear modelling it is assumed that each observation .. of a (univariate) time series ..,...,.. is a realization from a distribution of the exponential family wit

LOPE

S. Hellstr?m,J.-M. Pequignot,M. Hellstr?mreted by using a conditional independence graph (CIG). Some alternative association structures have been tested with GLIM by means of a discrete symmetric interaction model, obtaining a final GIC. The obtained results have been controlled with MIM by introducing a continuos random variable.