Sequential estimation in processes with independent increments
Sequential statistical structures
Some counterexamples concerning sufficiency and invariance.
Some remarks on sufficiency, invariance and conditional independence.
Some results for the quadratic analysis of Gaussian processes and applications
Some results on exponential families of Markov processes
Stability of invariant linearly sufficient statistics in the general Gauss-Markov model
Necessary and sufficient conditions are derived for the inclusions and to be fulfilled where , and , are some classes of invariant linearly sufficient statistics (Oktaba, Kornacki, Wawrzosek (1988)) corresponding to the Gauss-Markov models and , respectively.
Statistical models strongly-invariant under the action of a group.
Some basic results about invariance are given using quotient σ-fields. A strong kind of invariance is considered. Under appropriate conditions we obtain a sufficient statistics for models with such an invariance property.
Sufficiency and Unbiased Estimation.
Sufficiency in bayesian models
We consider some fundamental concepts of mathematical statistics in the Bayesian setting. Sufficiency, prediction sufficiency and freedom can be treated as special cases of conditional independence. We give purely probabilistic proofs of the Basu theorem and related facts.
Sufficiency in linear models
Sufficiency in sequential experimental design problems
Sufficient Data Reduction and Exponential Families.
Suffiziente Vergröberungen im schwach dominierten Fall.
Suffizienz bei Verschiebungsvarianten Schätzproblemen.
Sur l'existence d'une plus petite sous-tribu exhaustive par paire