  In this paper a robust version of the classical Wald test statistics for
linear hypothesis in the logistic regression model is introduced and its
properties are explored. We study the problem under the assumption of random
covariates although some ideas with non random covariates are also considered.
The family of tests considered is based on the minimum density power divergence
estimator instead of the maximum likelihood estimator and it is referred to as
the Wald-type test statistic in the paper. We obtain the asymptotic
distribution and also study the robustness properties of the Wald type test
statistic. The robustness of the tests is investigated theoretically through
the influence function analysis as well as suitable practical examples. It is
theoretically established that the level as well as the power of the Wald-type
tests are stable against contamination, while the classical Wald type test
breaks down in this scenario. Some classical examples are presented which
numerically substantiate the theory developed. Finally a simulation study is
included to provide further confirmation of the validity of the theoretical
results established in the paper.
