The HPQLIM Procedure

Tests on Parameters

In general, the tested hypothesis can be written as

\[  H_{0}: \mb{h} (\theta ) = 0  \]

where ${\mb{h} (\theta )}$ is an r $\times $ 1 vector-valued function of the parameters ${\theta }$ given by the r expressions that are specified in the TEST statement.

Let ${\hat{V}}$ be the estimate of the covariance matrix of ${\hat{\theta }}$. Let ${\hat{\theta }}$ be the unconstrained estimate of ${\theta }$ and ${\tilde{\theta }}$ be the constrained estimate of ${\theta }$ such that ${h(\tilde{\theta }) = 0}$. Let

\[  A(\theta ) = {{\partial } h(\theta )}/{{\partial } \theta }~ |_{\hat{\theta }}  \]

Using this notation, the test statistics for the three types of tests are computed as follows.

  • The Wald test statistic is defined as

    \[  W = h^{'}(\hat{\theta })\big \lgroup { {A(\hat{\theta }) \hat{V} A^{'} (\hat{\theta })} }\big \rgroup ^{-1} h(\hat{\theta })  \]

  • The Lagrange multiplier test statistic is

    \[  LM = {\lambda }^{'} A(\tilde{\theta }) \tilde{V} A^{'}(\tilde{\theta }) {\lambda }  \]

    where ${{\lambda }}$ is the vector of Lagrange multipliers from the computation of the restricted estimate ${\tilde{\theta }}$.

  • The likelihood ratio test statistic is

    \[  LR = 2 \left({L(\hat{\theta }) - L(\tilde{\theta })}\right)  \]

    where ${\tilde{\theta }}$ represents the constrained estimate of ${\theta }$ and ${L}$ is the concentrated log-likelihood value.

The following statements use the TEST statement to perform a likelihood ratio test:

   proc hpqlim;
      model y = x1 x2 x3;
      test x1 = 0, x2 * .5 + 2 * x3 = 0 /lr;
   run;

For more information, see the section Tests on Parameters in SAS/ETS 13.2 User's Guide.