By Subhash Sharma
This e-book makes a speciality of whilst to exploit a number of the analytic innovations and the way to interpret the ensuing output from the main time-honored statistical programs (e.g., SAS, SPSS).
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Extra resources for Applied Multivariate Techniques
Example text
1 Direct likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stream 2: Inferential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Rubin’s classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Missing at random and the conditional predictive distribution .
J. A. and Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. New York: Wiley. Little, R. J. A. and Wang, Y. (1996). Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52, 98–111. , and Lipsitz, S. R. (1999). A pattern-mixture odds ratio model for incomplete categorical data. Communications in Statistics: Theory and Methods 28, 2843–2869. , Goetghebeur, E.
1 Rubin’s classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Missing at random and the conditional predictive distribution . . . . . . . . . 3 Not missing at random and selection models . . . . . . . . . . . . . . . . . . . Stream 3: Semi-Parametric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critique of Ad Hoc Methods . . . . . . . . . . . .