Although　the　t-type　estimator　is　a　kind　of　M-estimator　with　scale　optimization,　it　has　some　advantages　over　the　M-estimator.　In　this　article,　we　first　propose　a　t-type　joint　generalized　linear　model　as　a　robust　extension　to　the　classical　joint　generalized　linear　models　for　modeling　data　containing　extreme　or　outlying　observations.　Next,　we　develop　a　t-type　pseudo-likelihood　(TPL)　approach,　which　can　be　viewed　as　a　robust　version　to　the　existing　pseudo-likelihood　(PL)　approach.　To　determine　which　variables　significantly　affect　the　variance　of　the　response　variable,　we　then　propose　a　unified　penalized　maximum　TPL　method　to　simultaneously　select　significant　variables　for　the　mean　and　dispersion　models　in　t-type　joint　generalized　linear　models.　Thus,　the　proposed　variable　selection　method　can　simultaneously　perform　parameter　estimation　and　variable　selection　in　the　mean　and　dispersion　models.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency　and　the　oracle　property　of　the　regularized　estimators.　Simulation　studies　are　conducted　to　illustrate　the　proposed　methods.