Estimation for aggregate models: The aggregate Markov chain
- 18 December 1984
- journal article
- Published by Wiley in The Canadian Journal of Statistics / La Revue Canadienne de Statistique
- Vol. 12 (4) , 265-282
- https://doi.org/10.2307/3314810
Abstract
An argument in favour of projecting the score function for models involving incomplete data is presented. Projection is then applied to the aggregate Markov‐chain model resulting in weighted least‐squares estimators. The limit theory and efficiency of these estimators are studied using martingale limit theory.Keywords
This publication has 13 references indexed in Scilit:
- The information in aggregate data from Markov chainsBiometrika, 1984
- Least‐squares estimation of transition probabilities from aggregate dataThe Canadian Journal of Statistics / La Revue Canadienne de Statistique, 1984
- Robust estimation through estimating equationsBiometrika, 1984
- On the Estimation of the Parameters of Markov Probability Models Using Macro DataThe Annals of Statistics, 1983
- Dependent Central Limit Theorems and Invariance PrinciplesThe Annals of Probability, 1974
- Estimating the mean and standard deviation from a censored normal sampleBiometrika, 1967
- An Optimum Property of Regular Maximum Likelihood EstimationThe Annals of Mathematical Statistics, 1960
- Least Squares Estimation in Finite Markov ProcessesPsychometrika, 1959
- K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises TestsThe Annals of Mathematical Statistics, 1959
- Finite Markov Processes in PsychologyPsychometrika, 1952