com.numericalmethod.suanshu.stats.dlm.univariate

Class DLM

java.lang.Object

com.numericalmethod.suanshu.stats.dlm.univariate.DLM

All Implemented Interfaces:

java.io.Serializable

public class DLM
extends java.lang.Object
implements java.io.Serializable

This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification. A controlled DLM, for (t ≥ 1), is described by two equations: the observation and state equations.

Observation Equation:

y_t = F_t * x_t + v_t,

State Equation:

x_t = G_t * x_t-1 + H_t * u_t + w_t,

{y_t} are the observation vectors; {x_t} are the state vectors. F_t and G_t are known matrices of dimension (number of observations * number of states) and (number of states * number of states) respectively. {v_t} and {w_t} are two independent sequences of independent normal random vectors with mean zero and known variance matrices {V_t} and {W_t}, respectively; Furthermore, it is assumed that x₀ is independent of {v_t} and {w_t} and is normally distributed with mean m₀ and covariance matrix C₀, where m₀ is a vector of length the same as the number of states and C₀ is a matrix of dimension (number of states * number of states); u_t is an m-dimensional vector of control variables, i.e., the variables whose values can be regulated by the user, in order to obtain a desired level of the state x_t. H_t is a known matrix of coefficients, with dimension of (number of states * m).

See Also:

• Wikipedia: Kalman filter - Underlying dynamic system model
• G. Petris et al., "Dynamic Linear Models with R," New York, Springer, 2009, ch. 2, pp. 31-84.

, Serialized Form

Constructor Summary

Constructors

Constructor and Description
DLM(DLM that) Copy constructor.
DLM(double m0, double C0, ObservationEquation Yt, StateEquation Xt) Construct a univariate controlled dynamic linear model.

Method Summary

Modifier and Type	Method and Description
double	C0() Get the variance of x0.
int	getObsDimension() Get the dimension of observations.
ObservationEquation	getObservationModel() Get the observation model.
int	getStateDimension() Get the dimension of states.
StateEquation	getStateModel() Get the state model.
double	m0() Get the the mean of x0.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DLM

public DLM(double m0,
           double C0,
           ObservationEquation Yt,
           StateEquation Xt)

Construct a univariate controlled dynamic linear model.

Parameters:

m0 - the mean of x₀

C0 - the variance of x₀

Yt - the observation equation for the model

Xt - the state equation for the model

DLM

public DLM(DLM that)

Copy constructor.

Parameters:

that - a univariate controlled dynamic linear model

Method Detail

m0

public double m0()

Get the the mean of x₀.

Returns:

m0, the mean of x₀

C0

public double C0()

Get the variance of x₀.

Returns:

C0, the variance of x₀

getObsDimension

public int getObsDimension()

Get the dimension of observations.

Returns:

the dimension of observations

getStateDimension

public int getStateDimension()

Get the dimension of states.

Returns:

the dimension of states

getObservationModel

public ObservationEquation getObservationModel()

Get the observation model.

Returns:

the observation model

getStateModel

public StateEquation getStateModel()

Get the state model.

Returns:

the state model