RT Journal Article T1 Modal iterative estimation in linear models with unimodal errors and non-grouped and grouped data collected from different sources A1 Anido, Carmen A1 Valdés Sánchez, Teófilo A1 Rivero Rodríguez, Carlos AB In this paper we introduce an iterative estimation procedure based on conditional modes suitable to fit linear models when errors are known to be unimodal and, moreover, the dependent data stem from different sources and, consequently, may be either non-grouped or grouped with different classification criteria. The procedure requires, at each step, the imputation of the exact values of the grouped data and runs by means of a process that is similar to the EM algorithm with normal errors. The expectation step has been substituted with a mode step that avoids awkward integration with general errors and, in addition, we have substituted the maximisation step with a natural one which only coincides with it when the error distribution is normal. Notwithstanding the former modifications, we have proved that, on the one hand, the iterative estimating algorithm converges to a point which is unique and non-dependent on the starting values and, on the other hand, our final estimate, being an M-estimator, may enjoy good stochastic asymptotic properties such as consistency, boundness in L-2, and limit normality PB Springer SN 1133-0686 YR 2000 FD 2000 LK https://hdl.handle.net/20.500.14352/58327 UL https://hdl.handle.net/20.500.14352/58327 LA eng NO Ministry of Education and Culture, Spain DS Docta Complutense RD 8 abr 2025