Maximum Likelihood MT

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Maximum Likelihood MT

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MaxlikMT contains a set of procedures for the solution of the maximum likelihood problem with bounds on parameters. In MaxlikMT, the same procedure computing the log-likelihood or objective function will be used to compute analytical derivatives as w...
Overview

Maximum Likelihood MT

MaxlikMT contains a set of procedures for the solution of the maximum likelihood problem with bounds on parameters.

In MaxlikMT, the same procedure computing the log-likelihood or objective function will be used to compute analytical derivatives as well if they are being provided. Its return argument is a maxlikmtResults structure with three members, a scalar, or Nx1 vector containing the log-likelihood (or objective), a 1xK vector, or NxK matrix of first derivatives, and a KxK matrix or NxKxK array of second derivatives (it needs to be an array if the log-likelihood is weighted).

Of course the derivatives are optional, or even partially optional, i.e., you can compute a subset of the derivatives if you like and the remaining will be computed numerically. This procedure will have an additional argument which tells the function which to compute, the log-likelihood or objective, the first derivatives, or the second derivatives, or all three. This means that calculations in common will not have to be redone.

Platform: Windows, Mac and Linux.

Requirements: GAUSS/GAUSS Light v10 or higher; Linux requires v10.0.4 or higher.

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Features

Major Features of MaxLikMT

  • Structures
  • Simple bounds
  • Hypothesis testing for models with bounded parameters
  • Log-likelihood function
  • AlgorithmSecant algorithms
  • Line search methods
  • Weighted maximum likelihood
  • Active and inactive parameters
  • Bounds

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4 + 2 = enter the result ex:(3 + 2 = 5)



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