Multiple Imputation via Local Regression (Miles)
|Faculty/Professorship:||Fakultät Sozial- und Wirtschaftswissenschaften: Abschlussarbeiten|
|Publisher Information:||Bamberg : opus|
|Year of publication:||2017|
|Pages:||xiii, 73 ; Illustrationen, Diagramme|
Dissertation, Otto-Friedrich-Universität Bamberg, 2017
|Licence:||German Act on Copyright|
Methods for statistical analyses generally rely upon complete rectangular data sets. When the data are incomplete due to, e.g. nonresponse in surveys, the researcher must choose between three alternatives:
1. The analysis rests on the complete cases only: This is almost always the worst option. In, e.g. market research, missing values occur more often among younger respondents. Because relevant behavior such as media consumption or past purchases often correlates with age, a complete case analysis provides the researcher with misleading answers.
2. The missing data are imputed (i.e., filled in) by the application of an ad-hoc method: Ad-hoc methods range from filling in mean values to applying nearest neighbor techniques. Whereas filling in mean values performs poorly, nearest neighbor approaches bear the advantage of imputing plausible values and work well in some applications. Yet, ad-hoc approaches generally suffer from two limitations: they do not apply to complex missing data patterns, and they distort statistical inference, such as t-tests, on the completed data sets.
3. The missing data are imputed by the application of a method that is based on an explicit model: Such model-based methods can cope with the broadest range of missing data problems. However, they depend on a considerable set of assumptions and are susceptible to their violations.
This dissertation proposes the two new methods
|GND Keywords:||Datenerhebung ; Fehlende Daten ; Regressionsanalyse|
|Keywords:||Multiple Imputation, Predictive Mean Matching, Sequential Regressions, Local Regression, Distance-Aided Donor Selection|
|DDC Classification:||310 Statistics|
|RVK Classification:||QH 235|
|Year of publication:||29. November 2017|
originated at the
University of Bamberg
University of Bamberg