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Small sample estimation of regression parameters in the three‐variable linear model, with incomplete observations
Author(s) -
Dagenais Marcel G.
Publication year - 1974
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314692
Subject(s) - linear regression , monte carlo method , estimator , proper linear model , statistics , regression analysis , variables , linear model , sample (material) , mathematics , computer science , econometrics , bayesian multivariate linear regression , chemistry , chromatography
Abstract This paper describes a new method for estimating the parameters, in linear regression models containing one dependent and two independent variables, when some observations are incomplete. The proposed technique is a markedly improved version of an approach described in previous papers [2, 3]. Results of Monte Carlo experiments suggest that the adopted modifications enhance considerably the small sample performance of the estimators.