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Full-Text Articles in Physical Sciences and Mathematics
Program For Missing Data In The Multivariate Normal Distribution, Chi-Ping Lu
Program For Missing Data In The Multivariate Normal Distribution, Chi-Ping Lu
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Missing data can often cause many problems in research work. Therefore for carrying out analysis, some procedure for obtaining estimates in the presence of missing data should be applied. Various theories and techniques have been developed for different types of problems.
Analysis of the Multivariate Normal Distribution with missing data is one of the areas studied. It has been discussed earlier by Wilkes (1932), Lord (1955), Edgett (1956) and Hartley (1958). They have established some basic concepts and an outline in the way of estimation.
In the last ten years, A. A. Afifi and R. M. Elasfoff also have contributed …
The Evaluation Of Glasser's Maximum Likelihood Method On Missing Data In Regression, Gayle M. Yamasaki
The Evaluation Of Glasser's Maximum Likelihood Method On Missing Data In Regression, Gayle M. Yamasaki
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Missing data in regression is often a problem to research workers because standard regression methods are applicable only to complete data sets. At present there are three general methods for solving the problem of missing data.
At first, the reduced data method, reduces the incomplete data set to a complete data set before analyzing. Although this method is very simple to apply, substantial amounts of information are sometimes lost when data is eliminated. This results in less precise estimates of the regression parameters.
The second method, generalized least squares, estimates the missing values through least squares techniques, thus obtaining a …