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    Mark S. Handcock "Statistical Methods For Combining Survey and Population-Level Data"


    In many situations information from a sample of individuals can be supplemented by information from population level data on the relationship of the explanatory variable with the dependent variables. Sources of population level data include a census, vital events registration systems and other governmental administrative record systems. They contain too few variables, however, to estimate demographically interesting models. Thus in a typical situation the estimation is done by using sample survey data alone, and the information from complete enumeration procedures is ignored. Sample survey data, however, are subjected to sampling error and bias due to non- response, whereas population level data are comparatively free of sampling error and typically less biased from the effects of non-response.

    In this talk we will review statistical methods for the incorporation of population level information and show it can lead to statistically more accurate estimates and better inference. Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are non-linear, making the task of maximum likelihood estimation more difficult. We present an alternative approach exploiting the notion of an empirical likelihood.

    We give an application to demographic hazard modeling by combining panel survey data with birth registration data to estimate annual birth probabilities by parity.

    This is joint work with Sanjay Chaudhuri (National University of Singapore), and Michael S. Rendall (RAND Corporation).



    BIO:

    Mark S. Handcock is Professor of Statistics at the University of California - Los Angeles. He received his B.Sc. from the University of Western Australia and his Ph.D. from the University of Chicago.

    Dr. Handcock’s research involves methodological development, and is based largely on motivation from questions in the social sciences, demography and epidemiology. He has published extensively on network models and inference as well as network sampling methods. He is a Fellow of the American Statistical Association.


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