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ParameterEstimator

ParameterEstimator
is an option to EstimatedDistribution and FindDistributionParameters that specifies what parameter estimator to use.
MORE INFORMATION
  • The following basic settings can be used:
"MaximumLikelihood"maximize the log-likelihood function
"MethodOfMoments"match raw moments
"MethodOfCentralMoments"match central moments
"MethodOfCumulants"match cumulants
"MethodOfFactorialMoments"match factorial moments
  • The maximum likelihood method will maximize the log-likelihood function where are the distribution parameters and is the PDF of the symbolic distribution.
  • The method of moments solves , , where is the ^(th) sample moment and is the ^(th) moment of the distribution with parameters .
  • The different methods of moments include: , , , or .
  • With ParameterEstimator, the moment orders specified by list are used for the method of moments estimator mm.
  • For univariate distributions, the moment orders should be a list of positive integers.
  • For -dimensional distributions, the moment orders should be length lists of non-negative integers with each list summing to a positive number.
  • Possible solver settings for include:
Automaticautomatically chosen solver
"FindMaximum"use FindMaximum to maximize log-likelihood
"FindRoot"use FindRoot to solve likelihood equations
"NMaximize"use NMaximize to maximize log-likelihood
  • Possible solver settings for , , , and include:
Automaticautomatically chosen solver
"FindRoot"use FindRoot to solve moment equations
"NSolve"use NSolve to solve moment equations
"Solve"use Solve to solve moment equations
  • The Automatic setting uses a solver or combination of solvers based on the distribution and the parameters to be estimated.
  • With the setting ParameterEstimator->{"estimator", Method->{"solver", opts}}, additional options can be given for the solver.
EXAMPLESCLOSE ALL
Basic Examples   (1)
Construct a distribution using maximum likelihood parameter estimates:
Use estimates based on method of moments:
Plot the difference between densities for the two estimates:
Construct a distribution using maximum likelihood parameter estimates:
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Use estimates based on method of moments:
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Plot the difference between densities for the two estimates:
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Scope   (4)
Obtain the maximum likelihood estimates using the default method:
Use NMaximize to obtain the estimates:
Obtain the maximum likelihood estimates using the default method:
Use FindMaximum with EvaluationMonitor to extract sampled points:
Visualize the sequences of sampled and values:
Estimate parameters by matching raw moments:
Other moment-based methods typically give similar results:
Estimate parameters using method of moments with default moments:
Use the first and fourth moments:
Use the second and third factorial moments:
Properties & Relations   (2)
Use FindDistributionParameters to get the maximum likelihood estimate:
Obtain the estimate by maximizing the log-likelihood directly:
Compute the maximized value from the FindDistributionParameters estimate:
Obtain the method-of-moments estimate for data:
Solve for parameters by matching moments when a closed form exists:
Obtain the symbolic result:
Compute the moments for data:
Substitute data moments to get the method-of-moments estimate:
Solve the moment equations numerically:
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