normalFisherByHessian

Compute Fisher information matrix by the negative expected value of Hessian matrix in Normal distribution.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test a sample against Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Payman Nickchi

gofedf

Goodness of Fit Tests Based on Empirical Distribution Functions

Richard Lockhart

normalFisherByHessian function

<dl><dt>theta</dt>
<dd>a numeric vector of length two, containing MLE of parameters in Normal dist</dd></dl>

Arguments

Compute Fisher information matrix by the negative expected value of Hessian matrix in Normal distribution. — normalFisherByHessian

<dl>

<dt>theta</dt>
<dd>a numeric vector of length two, containing MLE of parameters in Normal dist</dd>

</dl>

normalFisherByHessian: Compute Fisher information matrix by the negative expected value of Hessian matrix in Normal distribution.

Description

Usage

Value

Arguments