Finite mixture of partially linear model and finite mixture of experts models offer flexible tools for analyzing heterogeneous data, especially when covariates influence subgroup structure. Partially linear models further enhance this flexibility by combining linear and nonlinear effects. In many applications, however, censoring and departures from normality complicate estimation. To address these issues, we introduce a semiparametric mixture of censored partially linear expert models based on the two-piece scale mixture of normal family, which can accommodate skewness, heavy tails, and censoring. The proposed framework enables closed-form expressions in the E-step of an ECME algorithm, avoiding MCMC-based approximations and yielding stable maximum likelihood estimates. Simulation studies and real data analyses demonstrate the effectiveness of the proposed method in settings with both heterogeneity and censoring.

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