com.imsl.stat.distributions

Class MaximumLikelihoodEstimation

java.lang.Object

com.imsl.stat.distributions.MaximumLikelihoodEstimation

All Implemented Interfaces:

Serializable, Cloneable

public class MaximumLikelihoodEstimation
extends Object
implements Serializable, Cloneable

Maximum likelihood parameter estimation

See Also:

Example 1, Example 2, Example 3, Serialized Form

Constructor Summary

Constructors

Constructor and Description
MaximumLikelihoodEstimation(double[] x, ProbabilityDistribution pd) Constructor for maximum likelihood estimation

Method Summary

Methods

Modifier and Type	Method and Description
void	compute() Computes the maximum likelihood estimates.
double[]	getEstimates() Returns the parameter estimates.
double[][]	getHessian() Returns the hessian of the log-likelihood function evaluated at the current parameter estimates.
double	getMinusLogLikelihood() Returns minus the log-likelihood evaluated at the parameter estimates.
double[]	getStandardErrors() Returns the approximate standard errors of the maximum likelihood estimates.
double[][]	getVarCov() Returns the approximate variance-covariance matrix of the maximum likelihood estimates.
void	setExact(boolean exact) Sets the flag indicating whether or not the PDF supplies the exact gradient and hessian.
void	setGuess(double[] guess) Sets the guess or starting values of the parameters.
void	setSample(double[] x) Sets the sample data to use in the estimation procedure.

Methods inherited from class java.lang.Object

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

Constructor Detail

MaximumLikelihoodEstimation

public MaximumLikelihoodEstimation(double[] x,
                           ProbabilityDistribution pd)

Constructor for maximum likelihood estimation

Parameters:

x - a double array containing the sample observations

pd - an instance of ProbabilityDistribution

Method Detail

compute

public void compute()
             throws MinConNLP.ConstraintEvaluationException,
                    MinConNLP.ObjectiveEvaluationException,
                    MinConNLP.WorkingSetSingularException,
                    MinConNLP.QPInfeasibleException,
                    MinConNLP.PenaltyFunctionPointInfeasibleException,
                    MinConNLP.LimitingAccuracyException,
                    MinConNLP.TooManyIterationsException,
                    MinConNLP.BadInitialGuessException,
                    MinConNLP.IllConditionedException,
                    MinConNLP.SingularException,
                    MinConNLP.LinearlyDependentGradientsException,
                    MinConNLP.NoAcceptableStepsizeException,
                    MinConNLP.TerminationCriteriaNotSatisfiedException

Computes the maximum likelihood estimates.

Throws:

MinConNLP.ConstraintEvaluationException - Constraint evaluation returns an error with current point.

MinConNLP.ObjectiveEvaluationException - Objective evaluation returns an error with current point.

MinConNLP.WorkingSetSingularException - Working set is singular in dual extended QP.

MinConNLP.QPInfeasibleException - QP problem seemingly infeasible.

MinConNLP.PenaltyFunctionPointInfeasibleException - Penalty function point infeasible.

MinConNLP.LimitingAccuracyException - Limiting accuracy reached for a singular problem.

MinConNLP.TooManyIterationsException - Maximum number of iterations exceeded.

MinConNLP.BadInitialGuessException - Penalty function point infeasible for original problem. Try new initial guess.

MinConNLP.IllConditionedException - Problem is singular or ill-conditioned.

MinConNLP.SingularException - Problem is singular.

MinConNLP.LinearlyDependentGradientsException - Working set gradients are linearly dependent.

MinConNLP.NoAcceptableStepsizeException - No acceptable stepsize in [SIGMA,SIGLA].

MinConNLP.TerminationCriteriaNotSatisfiedException - Termination criteria are not satisfied.

getEstimates

public double[] getEstimates()

Returns the parameter estimates.

Returns:

a double array containing the parameter estimates

getHessian

public double[][] getHessian()

Returns the hessian of the log-likelihood function evaluated at the current parameter estimates.

Returns:

a double matrix containing the hessian matrix

getMinusLogLikelihood

public double getMinusLogLikelihood()

Returns minus the log-likelihood evaluated at the parameter estimates.

Returns:

a double, the minus log-likelihood

getStandardErrors

public double[] getStandardErrors()
                           throws SingularMatrixException

Returns the approximate standard errors of the maximum likelihood estimates.

Returns:

a double array containing the standard errors

Throws:

SingularMatrixException - The matrix is singular.

getVarCov

public double[][] getVarCov()
                     throws SingularMatrixException

Returns the approximate variance-covariance matrix of the maximum likelihood estimates. The approximation is the negative inverse hessian of the log-likelihood.

Returns:

a double matrix containing the approximate variance-covariance matrix

Throws:

SingularMatrixException

setExact

public void setExact(boolean exact)

Sets the flag indicating whether or not the PDF supplies the exact gradient and hessian.

Parameters:

exact - a boolean. When true, the function expects the pdf to supply the analytic gradient and hessian calculation.

Default: exact = false.

setGuess

public void setGuess(double[] guess)

Sets the guess or starting values of the parameters.

Parameters:

guess - a double array of the same length as the parameters containing proper starting values for the optimization

setSample

public void setSample(double[] x)

Sets the sample data to use in the estimation procedure.

Parameters:

x - a double array containing sample observations of the random variable