Influence of incomplete observations in multiple linear regression

Weichung Joseph Shih

Statistics & Probability Letters, 1989, vol. 8, issue 2, 171-174

Abstract: This paper is concerned with the influence of incomplete data due to random missing values in the multiple linear regression problem. Using the idea of Hampel's influence function, a partial influence function is derived and shown to be useful in several indications. Comparisons with the complete data situation and with the empirical case-deletion distance measure are also given.

Keywords: missing; values; EM; algorithm; influence; function; multiple; linear; regression (search for similar items in EconPapers)
Date: 1989
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