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Keyword: missing data;
3 results found.
Likelihood ratio inference for missing data models
Karun Adusumilli and Taisuke Otsu
Missing or incomplete outcome data is a ubiquitous problem in biomedical and social sciences. Under the missing at random setup, inverse probability weighting is widely applied to estimate and make inference on the popul...Read more...
30 October 2018
Nonparametric Inference for Unbalanced Time Series Data
This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a sta...Read more...
Messy Time Series: A Unified Approach - (Now published in 'Advances in Econometrics', 13 (1998)pp.103-143.)
Andrew C Harvey, Siem Jan Koopman and J Penzer
Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrin...Read more...