Likelihood Imputation

Abstract

The method of likelihood imputation is devised under the framework of latent structure models where the observation is a statistic of the complete data which can only be speci®ed on a latent basis. The imputed data set is chosen to differ least from the observed one in their information contentsÐa concept with general implications for the analysis of incomplete-data. In contrast to the standard conditional-mean single imputation, our procedure depends on an entire likelihood region instead of any single point in it, and yields consistent parameter estimators nevertheless. We explain its implementations and illustrate with data from panel surveys and linear regression with censorship. We also discuss its potentials in sensitivity analysis.

Evaluation of Measurement Precision from Stationary Baseline Noise in Instrumental Analyses

“…27,28 The optimization criterion used was the theoretically evaluated measurement relative standard deviation. 27,28 In such situations, the use of this methodology can circumvent repeated experiments with real samples, helping to improve the global environment by saving natural resources. In general, however, if either of conditions (i) and (ii), mentioned at the beginning of this section, is not met, the uncertainty evaluation would be no avail.…”

“…Fortunately, these unfavorable facts are not so serious in practice, since the applicability of the relevant methods proposed has been proved to be remarkable in analytical chemistry so far. [5][6][7][8][9][10]21,[26][27][28] …”