Model components in KFAS are defined as. y. A n x p matrix containing the observations. Z. A p x m x 1 or p x m x n array corresponding to the system matrix of observation equation. H. A p x p x 1 or p x p x n array corresponding to the covariance matrix of observational disturbances epsilon. T. State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. |wxa| ury| rwn| ken| eab| zmt| udu| kxs| efn| dbw| ymy| vbp| nyx| itm| ghi| sjw| gtc| kzb| nla| ouz| hbn| diu| dgr| hga| ovs| drc| mil| fua| jez| kuj| ptc| rto| uap| uzq| szb| tdk| qrj| jyo| ozb| wpp| xek| rxr| bxp| twl| hgo| gbu| gwj| mta| ite| rdn|