IASS Webinar 36: Latent Variable Models for Finite Population Inference

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Date(s) - 31/01/2024
2:00 pm - 3:30 pm

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IASS Webinar 36: Latent Variable Models for Finite Population Inference


Speaker: Maria Giovanna Ranalli, Associate Professor of Statistics,  University of Perugia (Italy)

31 January at 2pm – 3:30pm (CET)


All are invited to the webinar, organised by the International Association of Survey Statisticians.

Please register for the IASS Webinar at:

https://bit.ly/iass-webinar-36 [bit.ly]

After registering, you will receive a confirmation email containing information about joining the webinar. There will be time for questions. The webinar will be recorded and made available on the IASS and ISI web site. See below for the abstract and biography of the speakers.


Webinar Abstract

The use of latent variable models has become common practice in estimation from complex surveys, particularly to address small area estimation problems and non-sampling errors. In their basic definition, the assumption of Normality is made for the variable of interest and/or for the random effect(s) introduced to account for unobserved heterogeneity. In this talk, I will discuss the use alternative latent variable models, such as latent class models and latent trait models in estimation from complex surveys. Latent class models are useful tools to deal with (possibly non-ignorable) unit nonresponse to build better response homogeneity groups and using, for example, generalized calibration, when measurement error on the response variables is suspected. Latent trait models can be used to obtain a measure of the “attitude to respond” to a survey that can be used as a covariate in response propensity estimation. Finally, these models are very useful when the variable of interest is not directly observable, such as disability, social integration, educational poverty, and/or measured with error by means of a set of binary/categorical variables. I will present applications of these methods to Italian Household Surveys, such as the Survey on Households Income and Wealth and the survey on Health Conditions and Appeal to Medicare, and discuss their use to handle the process of integration of administrative and survey data for the production of official statistics.




Maria Giovanna Ranalli is Associate Professor of Statistics at the University of Perugia (Italy). Her research interests focus on statistical methods for inference for finite populations, on their socio-economic, environmental and health applications, and for the production of official statistics. She has contributed particularly to the literature on the use of nonparametric regression methods for finite populations and on small area estimation. Over the years, she has advised several institutions on survey methods, among others ISTAT (Italian National Statistical Office), where she is a member of the Methodology Advisory Board, OECD, the European Central Bank, FAO. She has (gladly) been a member of the Executive Committee of IASS between 2021 and 2023.