Titlebook: Smoothness Priors Analysis of Time Series; Genshiro Kitagawa,Will Gersch Book 1996 Springer Science+Business Media New York 1996 Likelihoo

北极人

灾祸

Vertebra

The Smoothness Priors Concept,0a) in which the framework initiated by Shiller was continued Akaike (1980a) was a quasi-Bayesian Gaussian disturbances linear regression, least squares computations, model framework. Stochastic difference equation constraints were placed on the prior distributions of the model parameters. The criti

GLUT

Scalar Least Squares Modeling,linear Gaussian modeling. Smoothness priors trend estimation for scalar time series is treated in Section 4.1. There, the smoothness priors constraint is expressed as a k-th order random walk with a normally distributed zero-mean, unknown variance perturbation. The (normalized) variance is a hyperpa

jettison

Linear Gaussian State Space Modeling,own. Model identification or, computation of the likelihood of the model is also treated. Some of the well known state space models that are used in this book as well as state space modeling of missing observations and a state space model for unequally spaced time series are shown. The final section

Ophthalmoscope

General State Space Modeling,because of the need to model time series with abrupt discontinuities, and time series with outliers and to model time series whose state and or observation processes were nonlinear. The general state space model and its recursive formulas for prediction, filtering and smoothing are treated in Sectio

两种语言

Applications of Linear Gaussian State Space Modeling,Canadian lynx data by an AR state space model, the modeling of irregularly spaced data and an example of the decomposition of an observed time series into a signal, background noise and observation noise are shown.

得罪人