xlinear

Class DeltaMethod

java.lang.Object

xlinear.DeltaMethod

@Data
public class DeltaMethod
extends java.lang.Object

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	DeltaMethod.Transformation

Constructor Summary

Constructors

Constructor and Description
DeltaMethod(Matrix data, AutoDiff.Differentiable transformation)

Method Summary

Modifier and Type	Method and Description
double	asymptoticVariance()
double	confidenceIntervalRadius() Build a 95% confidence interval around the value given by estimate()
double	confidenceIntervalRadius(double coverage) Construct the radius of an interval I such that asymptotically in n, P(Z \in I) = coverage The interval is around the value given by estimate(), i.e.
static Matrix	covar(Matrix data) n = number of observations p = number of parameters Input: nxp matrix Output: pxp covar matrix
org.apache.commons.math3.analysis.differentiation.DerivativeStructure	derivativeStructure()
boolean	equals(java.lang.Object obj)
double	estimate()
Matrix	getData()
AutoDiff.Differentiable	getTransformation()
int	hashCode()
Matrix	means()
int	n()
int	p()
java.lang.String	toString()

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

DeltaMethod

public DeltaMethod(Matrix data,
                   AutoDiff.Differentiable transformation)

Method Detail

confidenceIntervalRadius

public double confidenceIntervalRadius()

Build a 95% confidence interval around the value given by estimate()

confidenceIntervalRadius

public double confidenceIntervalRadius(double coverage)

Construct the radius of an interval I such that asymptotically in n, P(Z \in I) = coverage The interval is around the value given by estimate(), i.e. I = [estimate - confidenceIntervalRadius, estimate + confidenceIntervalRadius]

estimate

public double estimate()

asymptoticVariance

public double asymptoticVariance()

n

public int n()

p

public int p()

covar

public static Matrix covar(Matrix data)

n = number of observations p = number of parameters Input: nxp matrix Output: pxp covar matrix

means

public Matrix means()

derivativeStructure

public org.apache.commons.math3.analysis.differentiation.DerivativeStructure derivativeStructure()

hashCode

@Pure
public int hashCode()

Overrides:

hashCode in class java.lang.Object

equals

@Pure
public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object

toString

@Pure
public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getData

@Pure
public Matrix getData()

getTransformation

@Pure
public AutoDiff.Differentiable getTransformation()