Least-Squares Theory Based on General Distributional Assumptions with an Application to the Incomplete Observations Problem
- 1 March 1985
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 50 (1) , 25-36
- https://doi.org/10.1007/bf02294145
Abstract
The linear regression model y=β′x+ ε is reanalyzed. Taking the modest position that β′x is an approximation of the “best” predictor of y we derive the asymptotic distribution of b and R2, under mild assumptions.The method of derivation yields an easy answer to the estimation of β from a data set which contains incomplete observations, where the incompleteness is random.Keywords
This publication has 13 references indexed in Scilit:
- Models and methods for the analysis of correlation coefficientsJournal of Econometrics, 1983
- Some comments on maximum likelihood and partial least squares methodsJournal of Econometrics, 1983
- Simultaneous equation systems as moment structure modelsJournal of Econometrics, 1983
- The asymptotic distribution of elements of a correlation matrix: Theory and applicationBritish Journal of Mathematical and Statistical Psychology, 1982
- Models for Nonresponse in Sample SurveysJournal of the American Statistical Association, 1982
- Bootstrapping Regression ModelsThe Annals of Statistics, 1981
- The Poverty Line--A Pilot Survey in EuropeThe Review of Economics and Statistics, 1980
- Introductory EconometricsPublished by Springer Nature ,1978
- Linear Statistical Inference and its ApplicationsPublished by Wiley ,1973
- ON CERTAIN PROBABLE ERRORS AND CORRELATION COEFFICIENTS OF MULTIPLE FREQUENCY DISTRIBUTIONS WITH SKEW REGRESSIONBiometrika, 1916