pgam.fit {pgam} | R Documentation |
Estimate one-step ahead expectation and variance of y_{t} conditional on observed time series until the instant t-1.
pgam.fit(w, y, eta, partial.resid)
w |
estimate of discount factor omega of a Poisson-Gamma model |
y |
observed time series which is the response variable of the model |
eta |
semiparametric predictor |
partial.resid |
type of partial residuals. |
Partial residuals for semiparametric estimation is extracted. Those are regarded to the parametric partition fit of the model. Available types are raw
, pearson
and deviance
. The type raw
is prefered. Properties of other form of residuals not fully tested. Must be careful on choosing it.
See details in predict.pgam
and residuals.pgam
.
yhat |
vector of one-step ahead prediction |
resid |
vector partial residuals |
This function is not intended to be called directly.
Washington Leite Junger
wjunger@ims.uerj.br
Harvey, A. C., Fernandes, C. (1989) Time series models for count data or qualitative observations. Journal of Business and Economic Statistics, 7(4):407–417
Harvey, A. C. (1990) Forecasting, structural time series models and the Kalman Filter. Cambridge, New York
Campos, E. L., De Leon, A. C. M. P., Fernandes, C. A. C. (2003) Modelo Poisson-Gama para Séries Temporais de Dados de Contagem - Teoria e Aplicações. 10a ESTE - Escola de Séries Temporais e Econometria
Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach. Chapman and Hall, London
Junger, W. L. (2004) Modelo Poisson-Gama Semi-Paramétrico: Uma Abordagem de Penalização por Rugosidade. MSc Thesis. Rio de Janeiro, PUC-Rio, Departamento de Engenharia Elétrica
pgam
, residuals.pgam
, predict.pgam